Search results for: revenue optimization

Authors: Ahmed Elrewainy

Abstract:

Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no information about the given data cube. Sparsity is one of the recent approaches used in the source recovery or unmixing techniques. The l₁-norm optimization problem “basis pursuit” could be used as a sparsity-based approach to solve this unmixing problem where the endmembers is assumed to be sparse in an appropriate domain known as dictionary. This optimization problem is solved using proximal method “iterative thresholding”. The l₁-norm basis pursuit optimization problem as a sparsity-based unmixing technique was used to unmix real and synthetic hyperspectral data cubes.

Keywords: basis pursuit, blind source separation, hyperspectral imaging, spectral unmixing, wavelets

Procedia PDF Downloads 195

Authors: Mohammad H. Almomani

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In this paper, we argue the effect of the initial sample size, and the increment in simulation samples on the performance of a sequential approach that used in selecting the top m designs when the number of alternative designs is very large. The sequential approach consists of two stages. In the first stage the ordinal optimization is used to select a subset that overlaps with the set of actual best k% designs with high probability. Then in the second stage the optimal computing budget is used to select the top m designs from the selected subset. We apply the selection approach on a generic example under some parameter settings, with a different choice of initial sample size and the increment in simulation samples, to explore the impacts on the performance of this approach. The results show that the choice of initial sample size and the increment in simulation samples does affect the performance of a selection approach.

Keywords: Large Scale Problems, Optimal Computing Budget Allocation, ordinal optimization, simulation optimization

Procedia PDF Downloads 355

Authors: Yihao Kuang, Bowen Ding

Abstract:

With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

Procedia PDF Downloads 243

Authors: Bahram Fathi, Karim Alizadeh, Azam Mohammadbagheri

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The aim of this paper is to investigate the role of oil price shocks in explaining business cycles in Iran using a dynamic stochastic general equilibrium approach. This model incorporates both productivity and oil revenue shocks. The results indicate that productivity shocks are relatively more important to business cycles than oil shocks. The model with two shocks produces different values for volatility, but these values have the same ranking as that of the actual data for most variables. In addition, the actual data are close to the ratio of standard deviations to the output obtained from the model with two shocks. The results indicate that productivity shocks are relatively more important to business cycles than the oil shocks. The model with only a productivity shock produces the most similar figures in term of volatility magnitude to that of the actual data. Next, we use the Impulse Response Functions (IRF) to evaluate the capability of the model. The IRF shows no effect of an oil shock on the capital stocks and on labor hours, which is a feature of the model. When the log-linearized system of equations is solved numerically, investment and labor hours were not found to be functions of the oil shock. This research recommends using different techniques to compare the model’s robustness. One method by which to do this is to have all decision variables as a function of the oil shock by inducing the stationary to the model differently. Another method is to impose a bond adjustment cost. This study intends to fill that gap. To achieve this objective, we derive a DSGE model that allows for the world oil price and productivity shocks. Second, we calibrate the model to the Iran economy. Next, we compare the moments from the theoretical model with both single and multiple shocks with that obtained from the actual data to see the extent to which business cycles in Iran can be explained by total oil revenue shock. Then, we use an impulse response function to evaluate the role of world oil price shocks. Finally, I present implications of the findings and interpretations in accordance with economic theory.

Keywords: oil price, shocks, dynamic stochastic general equilibrium, Iran

Procedia PDF Downloads 438

Authors: Ali Shirazi, Robert A. Taylor, Stephen D. White, Graham L. Morrison

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In this paper, a detailed simulation model of a solar-powered triple-effect LiBr–H₂O absorption chiller is developed to supply both cooling and heating demand of a large-scale building, aiming to reduce the fossil fuel consumption and greenhouse gas emissions in building sector. TRNSYS 17 is used to simulate the performance of the system over a typical year. A combined energetic-economic-environmental analysis is conducted to determine the system annual primary energy consumption and the total cost, which are considered as two conflicting objectives. A multi-objective optimization of the system is performed using a genetic algorithm to minimize these objectives simultaneously. The optimization results show that the final optimal design of the proposed plant has a solar fraction of 72% and leads to an annual primary energy saving of 0.69 GWh and annual CO₂ emissions reduction of ~166 tonnes, as compared to a conventional HVAC system. The economics of this design, however, is not appealing without public funding, which is often the case for many renewable energy systems. The results show that a good funding policy is required in order for these technologies to achieve satisfactory payback periods within the lifetime of the plant.

Keywords: economic, environmental, multi-objective optimization, solar air-conditioning, triple-effect absorption chiller

Procedia PDF Downloads 238

Authors: Junjie Peng, Jinbao Chen, Shuai Kong, Danxu Liu

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On the basis of traditional resource allocation strategies, the usage of resources on physical servers in cloud data center is great uncertain. It will cause waste of resources if the assignment of tasks is not enough. On the contrary, it will cause overload if the assignment of tasks is too much. This is especially obvious when the applications are the same type because of its resource preferences. Considering CPU intensive application is one of the most common types of application in the cloud, we studied the optimization strategy for CPU intensive applications on the same server. We used resource preferences to analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can predict the execution time for CPU intensive applications which run simultaneously. Based on the prediction model, we proposed the method to select the appropriate number of applications for a machine. Experiments show that the model can predict the execution time accurately for CPU intensive applications. To improve the execution efficiency of applications, we propose a scheduling model based on priority for CPU intensive applications. Extensive experiments verify the validity of the scheduling model.

Keywords: cloud computing, CPU intensive applications, resource optimization, strategy

Procedia PDF Downloads 278

Authors: Tahsin A. H. Nishat, Raquib Ahsan

Abstract:

Sensitivity analysis of design parameters of the optimization procedure can become a significant factor while designing any structural system. The objectives of the study are to analyze the sensitivity of deck slab thickness parameter obtained from both the conventional and optimum design methodology of pre-stressed post-tensioned I-girder and deck system and to compare the relative significance of slab thickness. For analysis on conventional method, the values of 14 design parameters obtained by the conventional iterative method of design of a real-life I-girder bridge project have been considered. On the other side for analysis on optimization method, cost optimization of this system has been done using global optimization methodology 'Evolutionary Operation (EVOP)'. The problem, by which optimum values of 14 design parameters have been obtained, contains 14 explicit constraints and 46 implicit constraints. For both types of design parameters, sensitivity analysis has been conducted on deck slab thickness parameter which can become too sensitive for the obtained optimum solution. Deviations of slab thickness on both the upper and lower side of its optimum value have been considered reflecting its realistic possible ranges of variations during construction. In this procedure, the remaining parameters have been kept unchanged. For small deviations from the optimum value, compliance with the explicit and implicit constraints has been examined. Variations in the cost have also been estimated. It is obtained that without violating any constraint deck slab thickness obtained by the conventional method can be increased up to 25 mm whereas slab thickness obtained by cost optimization can be increased only up to 0.3 mm. The obtained result suggests that slab thickness becomes less sensitive in case of conventional method of design. Therefore, for realistic design purpose sensitivity should be conducted for any of the design procedure of girder and deck system.

Keywords: sensitivity analysis, optimum design, evolutionary operations, PC I-girder, deck system

Procedia PDF Downloads 137

Authors: Tareq Hamadneh, Jochen Merker, Hassan Al-Zoubi

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Bernstein's expansion for exponentials in Taylor functions provides lower and upper optimization values for the range of its original function. these values converge to the original functions if the degree is elevated or the domain subdivided. Taylor polynomial can be applied so that the exponential is a polynomial of finite degree over a given domain. Bernstein's basis has two main properties: its sum equals 1, and positive for all x 2 (0; 1). In this work, we prove the existence of fixed points for exponential functions in a given domain using the optimization values of Bernstein. The Bernstein basis of finite degree T over a domain D is defined non-negatively. Any polynomial p of degree t can be expanded into the Bernstein form of maximum degree t ≤ T, where we only need to compute the coefficients of Bernstein in order to optimize the original polynomial. The main property is that p(x) is approximated by the minimum and maximum Bernstein coefficients (Bernstein bound). If the bound is contained in the given domain, then we say that p(x) has fixed points in the same domain.

Keywords: Bernstein polynomials, Stability of control functions, numerical optimization, Taylor function

Procedia PDF Downloads 135

Authors: Karim Farahmandfar, Hamidolah Izadi, Mohammadreza Rezaei, Amin Ardali, Ebrahim Goshtasbi Rad, Khosro Jafarpoor

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Statistics illustrates that the higher amount of annual energy consumption is related to surmounting the demand in buildings. Therefore, it is vital to economize the energy consumption and also find the solution with regard to this issue. One of the systems for the sake of heating the building is floor heating. As a matter of fact, floor heating performance is based on convection and radiation. Actually, in addition to creating a favorable heating condition, this method leads to energy saving. It is the goal of this article to outline the constructal theory and introduce the optimization method in branch networks for floor heating. There are several steps in order to gain this purpose. First of all, the pressure drop through the two points of the network is calculated. This pressure drop is as a function of pipes diameter and other parameters. After that, the amount of heat transfer is determined. Consequently, as a result of the combination of these two functions, the final function will be determined. It is necessary to mention that flow is laminar.

Keywords: constructal theory, optimization, floor heating system, turbulent flow

Procedia PDF Downloads 319

Authors: Swarali Hingse, Shraddha Digole, Uday Annapure

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The present study investigates the biotransformation of ferulic acid to vanillin by Pycnoporus cinnabarinus and its optimization using one-factor-at-a-time method as well as statistical approach. Effect of various physicochemical parameters and medium components was studied using one-factor-at-a-time method. Screening of the significant factors was carried out using L25 Taguchi orthogonal array and then these selected significant factors were further optimized using response surface methodology (RSM). Significant media components obtained using Taguchi L25 orthogonal array were glucose, KH2PO4 and yeast extract. Further, a Box Behnken design was used to investigate the interactive effects of the three most significant media components. The final medium obtained after optimization using RSM containing glucose (34.89 g/L), diammonium tartrate (1 g/L), yeast extract (1.47 g/L), MgSO4•7H2O (0.5 g/L), KH2PO4 (0.15 g/L), and CaCl2•2H2O (20 mg/L) resulted in amplification of vanillin production from 30.88 mg/L to 187.63 mg/L.

Keywords: ferulic acid, pycnoporus cinnabarinus, response surface methodology, vanillin

Procedia PDF Downloads 383

Authors: Archana Thangavelu, Han-Ik Park, Young-Chul Park, Joon-Hong Park

Abstract:

In this numerical study, we have proposed a method for evaluation of backward extrusion process of pressure vessel made up of steel. Demand for lighter and stiffer products have been increasing in the last years especially in automobile engineering. Through detailed finite element analysis, effective stress, strain and velocity profile have been obtained with optimal range. The process design of a forward and backward extrusion axe-symmetric part has been studied. Forging is mainly carried out because forged products are highly reliable and possess superior mechanical properties when compared to normal products. Performing computational simulations of 3D hot forging with various dimensions of billet and optimization of weight is carried out using Taguchi Orthogonal Array (OA) Optimization technique. The technique used in this study can be used for newly developed materials to investigate its forgeability for much complicated shapes in closed hot die forging process.

Keywords: backward extrusion, hot forging, optimization, finite element analysis, Taguchi method

Procedia PDF Downloads 309

Authors: Xuran Zhang, Huiru Chen

Abstract:

In recent years, China has made great efforts to cultivate and develop the housing rental market, especially the rental-type public housing, which has been paid attention to by all sectors of the society. This paper takes Hangzhou rental-type public housing as the research object, and divides it into three development stages according to the different supply modes of rental-type public housing. Through data collection and field research, the paper summarizes the spatial characteristics of rental-type public housing from the five perspectives of spatial planning, spatial layout, spatial integration, spatial organization and spatial configuration. On this basis, the paper proposes the optimization of the spatial layout. The study concludes that the spatial layout of rental-type public housing should be coordinated with the development of urban planning. When planning and constructing, it is necessary to select more mixed construction modes, to be properly centralized, and to improve the surrounding transportation service facilities.  It is hoped that the recommendations in this paper will provide a reference for the further development of rental-type public housing in Hangzhou.

Keywords: Hangzhou, rental-type public housing, spatial distribution, spatial optimization

Procedia PDF Downloads 323

Authors: Lela Pintarić, Iva Rezić, Ana Vrsalović Presečki

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Considering an enormous increase of the use of metal nanoparticles with the exactly defined characteristics, the main goal of this research was to found the optimal and environmental friendly method of their synthesis. The synthesis of the inorganic core-shell nanoparticles was optimized as a model. The core-shell nanoparticles are composed of the enzyme core belted with the metal ions, oxides or salts as a shell. In this research, enzyme urease was the core catalyst and the shell nanoparticle was made of silver. Silver nanoparticles are widespread utilized and some of their common uses are: as an addition to disinfectants to ensure an aseptic environment for the patients, as a surface coating for neurosurgical shunts and venous catheters, as an addition to implants, in production of socks for diabetics and athletic clothing where they improve antibacterial characteristics, etc. Characteristics of synthesized nanoparticles directly depend on of their size, so the special care during this optimization was given to the determination of the size of the synthesized nanoparticles. For the purpose of the above mentioned optimization, sixteen experiments were generated by the Design of Experiments (DoE) method and conducted under various temperatures, with different initial concentration of the silver nitrate and constant concentration of the urease of two separate manufacturers. Synthesized nanoparticles were analyzed by the Nanoparticle Tracking Analysis (NTA) method on Malvern NanoSight NS300. Results showed that the initial concentration of the silver ions does not affect the concentration of the synthesized silver nanoparticles neither their size distribution. On the other hand, temperature of the experiments has affected both of the mentioned values.

Keywords: core-shell nanoparticles, optimization, silver, urease

Procedia PDF Downloads 313

Authors: Willi Zschiebsch, Alrik Dargel, Sebastian Spitzer, Philipp Johst, Robert Böhm, Niels Modler

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In this article, we investigate an approach that digitally links individual development process steps by using the drive shaft of an aircraft engine as a representative example of a fiber polymer composite. Such high-performance, lightweight composite structures have many adjustable parameters that influence the mechanical properties. Only a combination of optimal parameter values can lead to energy efficient lightweight structures. The development tools required for the Engineering Design Process (EDP) are often isolated solutions, and their compatibility with each other is limited. A digital framework is presented in this study, which allows individual specialised tools to be linked via the generated data in such a way that automated optimization across programs becomes possible. This is demonstrated using the example of linking geometry generation with numerical structural analysis. The proposed digital framework for automated design optimization demonstrates the feasibility of developing a complete digital approach to design optimization. The methodology shows promising potential for achieving optimal solutions in terms of mass, material utilization, eigenfrequency, and deformation under lateral load with less development effort. The development of such a framework is an important step towards promoting a more efficient design approach that can lead to stable and balanced results.

Keywords: digital linked process, composite, CFRP, multi-objective, EDP, NSGA-2, NSGA-3, TPE

Procedia PDF Downloads 76

Authors: Yihao Kuang, Bowen Ding

Abstract:

With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

Procedia PDF Downloads 67

Authors: Berfin Yildiz

Abstract:

These days, increasing global warming effects, limited amount of energy resources, etc., necessitates the awareness that must be present in every profession group. The architecture and construction sectors are responsible for both the embodied and operational energy of the materials. This responsibility has led designers to seek alternative solutions for energy-efficient material selection. The choice of energy-efficient material requires consideration of the entire life cycle, including the building's production, use, and disposal energy. The aim of this study is to investigate the method of material selection of external thermal insulation composite systems (ETICS). Embodied and in-use energy values of material alternatives were used for the evaluation in this study. The operational energy is calculated according to the u-value calculation method defined in the TS 825 (Thermal Insulation Requirements) standard for Turkey, and the embodied energy is calculated based on the manufacturer's Energy Performance Declaration (EPD). ETICS consists of a wall, adhesive, insulation, lining, mechanical, mesh, and exterior finishing materials. In this study, lining, mechanical, and mesh materials were ignored because EPD documents could not be obtained. The material selection problem is designed as a hypothetical volume area (5x5x3m) and defined as a multi-objective discrete optimization problem for external thermal insulation composite systems. Defining the problem as a discrete optimization problem is important in order to choose between materials of various thicknesses and sizes. Since production and use energy values, which are determined as optimization objectives in the study, are often conflicting values, material selection is defined as a multi-objective optimization problem, and it is aimed to obtain many solution alternatives by using Hypervolume (HypE) algorithm. The enrollment process started with 100 individuals and continued for 50 generations. According to the obtained results, it was observed that autoclaved aerated concrete and Ponce block as wall material, glass wool, as insulation material gave better results.

Keywords: embodied energy, multi-objective discrete optimization, performative design, thermal insulation

Procedia PDF Downloads 141

Authors: Dominika Crnjac Milic, Martina Martinovic, Vladimir Simovic

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The case study method in this paper shows the implementation of Information Technology (IT) and the Serial Shipping Container Code (SSCC) in a Croatian company that deals with logistics operations and provides logistics services in the cold chain segment. This company is aware of the sensitivity of the goods entrusted to them by the user of the service, as well as of the importance of speed and accuracy in providing logistics services. To that end, it has implemented and used the latest IT to ensure the highest standard of high-quality logistics services to its customers. Looking for efficiency and optimization of supply chain management, while maintaining a high level of quality of the products that are sold, today's users of outsourced logistics services are open to the implementation of new IT products that ultimately deliver savings. By analysing the positive results and the difficulties that arise when using this technology, we aim to provide an insight into the potential of this approach of the logistics service provider.

Keywords: logistics operations, serial shipping container code, information technology, cost optimization

Procedia PDF Downloads 360

Authors: Habib Muhammetoglu, I. Ethem Karadirek, Selami Kara, Ayse Muhammetoglu

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The problem of non-revenue water (NRW) from municipal water distribution networks is common in many countries such as Turkey, where the average yearly water losses are around 50% . Water losses can be divided into two major types namely: 1) Real or physical water losses, and 2) Apparent or commercial water losses. Total water losses in Antalya city, Turkey is around 45%. Methods: A research study was conducted to develop appropriate methodologies to reduce NRW. A pilot study area of about 60 thousands inhabitants was chosen to apply the study. The pilot study area has a supervisory control and data acquisition (SCADA) system for the monitoring and control of many water quantity and quality parameters at the groundwater drinking wells, pumping stations, distribution reservoirs, and along the water mains. The pilot study area was divided into 18 District Metered Areas (DMAs) with different number of service connections that ranged between a few connections to less than 3000 connections. The flow rate and water pressure to each DMA were on-line continuously measured by an accurate flow meter and water pressure meter that were connected to the SCADA system. Customer water meters were installed to all billed and unbilled water users. The monthly water consumption as given by the water meters were recorded regularly. Water balance was carried out for each DMA using the well-know standard IWA approach. There were considerable variations in the water losses percentages and the components of the water losses among the DMAs of the pilot study area. Old Class B customer water meters at one DMA were replaced by more accurate new Class C water meters. Hydraulic modelling using the US-EPA EPANET model was carried out in the pilot study area for the prediction of water pressure variations at each DMA. The data sets required to calibrate and verify the hydraulic model were supplied by the SCADA system. It was noticed that a number of the DMAs exhibited high water pressure values. Therefore, pressure reducing valves (PRV) with constant head were installed to reduce the pressure up to a suitable level that was determined by the hydraulic model. On the other hand, the hydraulic model revealed that the water pressure at the other DMAs cannot be reduced when complying with the minimum pressure requirement (3 bars) as stated by the related standards. Results: Physical water losses were reduced considerably as a result of just reducing water pressure. Further physical water losses reduction was achieved by applying acoustic methods. The results of the water balances helped in identifying the DMAs that have considerable physical losses. Many bursts were detected especially in the DMAs that have high physical water losses. The SCADA system was very useful to assess the efficiency level of this method and to check the quality of repairs. Regarding apparent water losses reduction, changing the customer water meters resulted in increasing water revenue by more than 20%. Conclusions: DMA, SCADA, modelling, pressure management, leakage detection and accurate customer water meters are efficient for NRW.

Keywords: NRW, water losses, pressure management, SCADA, apparent water losses, urban water distribution networks

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Authors: Essa Abrahim Abdulgader Saleem, Thien-My Dao

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The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA.

Keywords: reliability, optimization, meta-heuristic, genetic algorithm, redundancy

Procedia PDF Downloads 337

Authors: E. Tandis, E. Assareh

Abstract:

Designing the optimal shape of MW wind turbine blades is provided in a number of cases through evolutionary algorithms associated with mathematical modeling (Blade Element Momentum Theory). Evolutionary algorithms, among the optimization methods, enjoy many advantages, particularly in stability. However, they usually need a large number of function evaluations. Since there are a large number of local extremes, the optimization method has to find the global extreme accurately. The present paper introduces a new population-based hybrid algorithm called Genetic-Based Bees Algorithm (GBBA). This algorithm is meant to design the optimal shape for MW wind turbine blades. The current method employs crossover and neighborhood searching operators taken from the respective Genetic Algorithm (GA) and Bees Algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Different blade designs, twenty-one to be exact, were considered based on the chord length, twist angle and tip speed ratio using GA results. They were compared with BA and GBBA optimum design results targeting the power coefficient and solidity. The results suggest that the final shape, obtained by the proposed hybrid algorithm, performs better compared to either BA or GA. Furthermore, the accuracy and speed convergence increases when the GBBA is employed

Keywords: Blade Design, Optimization, Genetic Algorithm, Bees Algorithm, Genetic-Based Bees Algorithm, Large Wind Turbine

Procedia PDF Downloads 316

Authors: Ali Ramezani

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Balancing feeding system of plastic injection molding has overriding importance as it minimizes the process’s product defects such as weld line, shrinkage, sink marks and warpage. This article presents the difference between optimization of feeding system in identical multi-cavity molding and family molding using Moldflow Plastic Insight software. In this work, the effect of dimension, shape, position and type of gates and runners on the products quality was studied. The optimization was carried out by analyzing plastic injection molding process parameters, including melt temperature, mold temperature, cooling time, cooling temperature packing time and packing pressure. It was found that symmetrical feeding system is the most efficient shape for diminishing defects in identical multi-cavity molding. However, the same results were not concluded for family molding due to the differences between volume, mass, thickness and shape of cavities.

Keywords: balancing feeding system, family molding, multi-cavity, Moldflow, plastic injection

Procedia PDF Downloads 135

Authors: Seyed Sadegh Naseralavi, Mohsen Khatibinia

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In structural optimizations through meta-heuristic algorithms, analyses of structures are performed for many times. For this reason, performing the analyses in a time saving way is precious. The importance of the point is more accentuated in time-history analyses which take much time. To this aim, peak picking methods also known as spectrum analyses are generally utilized. However, such methods do not have the required accuracy either done by square root of sum of squares (SRSS) or complete quadratic combination (CQC) rules. The paper presents an efficient technique for evaluating the dynamic responses during the optimization process with high speed and accuracy. In the method, first by using a static equivalent of the earthquake, an initial design is obtained. Then, the displacements in the modal coordinates are achieved. The displacements are herein called basic modal displacements (MBD). For each new design of the structure, the responses can be derived by well scaling each of the MBD along the time and amplitude and superposing them together using the corresponding modal matrices. To illustrate the efficiency of the method, an optimization problems is studied. The results show that the proposed approach is a suitable replacement for the conventional time history and spectrum analyses in such problems.

Keywords: basic modal displacements, earthquake, optimization, spectrum

Procedia PDF Downloads 361

Authors: Bao Li, Aike Qiao, Gaoyang Li, Youjun Liu

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Background: Lumped parameter model (LPM) is a common numerical model for hemodynamic calculation. LPM uses circuit elements to simulate the human blood circulatory system. Physiological indicators and characteristics can be acquired through the model. However, due to the different physiological indicators of each individual, parameters in LPM should be personalized in order for convincing calculated results, which can reflect the individual physiological information. This study aimed to develop an automatic and effective optimization method to personalize the parameters in LPM of the blood circulatory system, which is of great significance to the numerical simulation of individual hemodynamics. Methods: A closed-loop LPM of the human blood circulatory system that is applicable for most persons were established based on the anatomical structures and physiological parameters. The patient-specific physiological data of 5 volunteers were non-invasively collected as personalized objectives of individual LPM. In this study, the blood pressure and flow rate of heart, brain, and limbs were the main concerns. The collected systolic blood pressure, diastolic blood pressure, cardiac output, and heart rate were set as objective data, and the waveforms of carotid artery flow and ankle pressure were set as objective waveforms. Aiming at the collected data and waveforms, sensitivity analysis of each parameter in LPM was conducted to determine the sensitive parameters that have an obvious influence on the objectives. Simulated annealing was adopted to iteratively optimize the sensitive parameters, and the objective function during optimization was the root mean square error between the collected waveforms and data and simulated waveforms and data. Each parameter in LPM was optimized 500 times. Results: In this study, the sensitive parameters in LPM were optimized according to the collected data of 5 individuals. Results show a slight error between collected and simulated data. The average relative root mean square error of all optimization objectives of 5 samples were 2.21%, 3.59%, 4.75%, 4.24%, and 3.56%, respectively. Conclusions: Slight error demonstrated good effects of optimization. The individual modeling algorithm developed in this study can effectively achieve the individualization of LPM for the blood circulatory system. LPM with individual parameters can output the individual physiological indicators after optimization, which are applicable for the numerical simulation of patient-specific hemodynamics.

Keywords: blood circulatory system, individual physiological indicators, lumped parameter model, optimization algorithm

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Authors: Abdelghani Choucha, Lakhdar Chaib, Salem Arif

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This paper treated the problem of power system stability with the aid of Static Synchronous Series Compensator (SSSC) installed in the transmission line of single machine infinite bus (SMIB) power system. A fractional order PID (FOPID) controller has been applied as a robust controller for optimal SSSC design to control the power system characteristics. Additionally, the SSSC based FOPID parameters are smoothly tuned using Invasive Weed Optimization algorithm (IWO). To verify the strength of the proposed controller, SSSC based FOPID controller is validated in a wide range of operating condition and compared with the conventional scheme SSSC-POD controller. The main purpose of the proposed process is greatly enhanced the dynamic states of the tested system. Simulation results clearly prove the superiority and performance of the proposed controller design.

Keywords: SSSC-FOPID, SSSC-POD, SMIB power system, invasive weed optimization algorithm

Procedia PDF Downloads 188

Authors: Abdul Halim Abdul Razik, Ali Elkamel, Leonardo Simon

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Wheat straw is one of the alternative feedstocks that may be utilized for bioethanol production especially when sustainability criteria are the major concerns. To increase market competitiveness, optimal supply chain plays an important role since wheat straw is a seasonal agricultural residue. In designing the supply chain optimization model, economic profitability of the thermochemical and biochemical conversion routes options were considered. It was found that torrefied pelletization with gasification route to be the most profitable option to produce bioethanol from the lignocellulosic source of wheat straw.

Keywords: bio-ethanol, optimization, supply chain, wheat straw

Procedia PDF Downloads 737

Authors: Roberto Machado

Abstract:

We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Hany Mohamed Abdu, Mohamed Hassan Gadallah, El-Giushi Mokhtar, Yehia Mahmoud Ismail

Abstract:

Laser beam cutting is one nontraditional machining process. This paper optimizes the parameters of Laser beam cutting machining parameters of Stainless steel (316L) by considering the effect of input parameters viz. power, oxygen pressure, frequency and cutting speed. Statistical design of experiments are carried in three different levels and process responses such as 'Average kerf taper (Ta)' and 'Surface Roughness (Ra)' are measured accordingly. A quadratic mathematical model (RSM) for each of the responses is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27 OA) are employed to search for an optimal parametric combination to achieve desired yield of the process. RSM models are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA) using MATLAB environment. Optimum solutions are compared with Taguchi Methodology results.

Keywords: optimization, laser cutting, robust design, kerf width, Taguchi method, RSM and DOE

Procedia PDF Downloads 620

Authors: Xiao-Li Liu, Ling-Yun Zhao, Xing-Jie Liang, Hai-Ming Fan

Abstract:

Owing to their unique properties, magnetic nanoparticles have been used as diagnostic and therapeutic agents for biomedical applications. Highly monodispersed magnetic nanoparticles with controlled particle size and surface coating have been successfully synthesized as a model system to investigate the effect of surface coating on the T2 relaxivity and specific absorption rate (SAR) under an alternating magnetic field, respectively. Amongst, by using mPEG-g-PEI to solubilize oleic-acid capped 6 nm magnetic nanoparticles, the T2 relaxivity could be significantly increased by up to 4-fold as compared to PEG coated nanoparticles. Moreover, it largely enhances the cell uptake with a T2 relaxivity of 92.6 mM-1s-1 for in vitro cell MRI. As for hyperthermia agent, SAR value increase with the decreased thickness of PEG surface coating. By elaborate optimization of surface coating and particle size, a significant increase of SAR (up to 74%) could be achieved with a minimal variation on the saturation magnetization (<5%). The 19 nm magnetic nanoparticles with 2000 Da PEG exhibited the highest SAR of 930 W•g-1 among the samples, which can be maintained in various simulated physiological conditions. This systematic work provides a general strategy for the optimization of surface coating of magnetic core for high performance MRI contrast agent and hyperthermia agent.

Keywords: magnetic nanoparticles, magnetic hyperthermia, magnetic resonance imaging, surface modification

Procedia PDF Downloads 510

Authors: Marin Mandić, Elis Sutlović, Tonći Modrić, Luka Stanić

Abstract:

With the restructuring and deregulation of the power system, storage owners, generation companies or private producers can offer their multiple services on various power markets and earn income in different types of markets, such as the day-ahead, real-time, ancillary services market, etc. During the COVID-19 pandemic, electricity prices, as well as ancillary services prices, increased significantly. The optimization of the energy storage operation was performed using a suitable model for simulating the operation of a pumped storage hydropower plant under market conditions. The objective function maximizes the income earned through energy arbitration, regulation-up, regulation-down and spinning reserve services. The optimization technique used for solving the objective function is mixed integer linear programming (MILP). In numerical examples, the pumped storage hydropower plant operation has been optimized considering the already achieved hourly electricity market prices from Nord Pool for the pre-pandemic (2019) and the pandemic (2020 and 2021) years. The impact of the electricity market prices during the COVID-19 pandemic on energy storage operation is shown through the analysis of income, operating hours, reserved capacity and consumed energy for each service. The results indicate the role of energy storage during a significant fluctuation in electricity and services prices.

Keywords: electrical market prices, electricity market, energy storage optimization, mixed integer linear programming (MILP) optimization

Procedia PDF Downloads 174

Authors: Mahdi Fakoor, Seyed Mohammad Navid Ghoreishi

Abstract:

Composite patching is a common way for reinforcing the cracked pipes and cylinders. The effects of composite patch reinforcement on fracture parameters of a cracked pipe depend on a variety of parameters such as number of layers, angle, thickness, and material of each layer. Therefore, stacking sequence optimization of composite patch becomes crucial for the applications of cracked pipes. In this study, in order to obtain the optimal stacking sequence for a composite patch that has minimum weight and maximum resistance in propagation of cracks, a coupled Multi-Objective Genetic Algorithm (MOGA) and Finite Element Method (FEM) process is proposed. This optimization process has done for longitudinal and transverse semi-elliptical cracks and optimal stacking sequences and Pareto’s front for each kind of cracks are presented. The proposed algorithm is validated against collected results from the existing literature.

Keywords: multi objective optimization, pareto front, composite patch, cracked pipe

Procedia PDF Downloads 312