Search results for: bacterial foraging optimization

Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

Abstract:

An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

Procedia PDF Downloads 783

Authors: Supriya Chatla, Anjana Male, Srikala Kamireddy

Abstract:

L-asparaginase (E.C.3.5.1.1) is an anti-cancer enzyme that has been purified and characterized for decades to study and evaluate its anti-carcinogenic activity against Hodgkin’s lymphoma. The present investigation deals with screening, isolation and optimization of L-asparaginase giving fungal strain of soil samples from different areas of AP, India. L-Aspariginase activity was estimated on the basis of the pink color surrounding the growing colony. A total of 132 colonies were screened and isolated from different samples. Based on the zone diameter, L-asparaginase activity is determined, L- asparaginase activity is optimized at 28oc and Immobilized Aspariginase had more potency than the free enzymes.

Keywords: aspariginase, anticancer enzyme, Isolation, optimization

Procedia PDF Downloads 68

Authors: Byeong-Sam Kim, Kyeongwoo Park

Abstract:

In this study, induced structural optimization is performed to compare the trade-off between wing weight and induced drag for wing panel extensions, construction of wing panel and winglets. The aerostructural optimization problem consists of parameters with strength condition, and two maneuver conditions using residual stresses in panel production. The results of kinematic motion analysis presented a homogenization based theory for 3D beams and 3D shells for wing panel. This theory uses a kinematic description of the beam based on normalized displacement moments. The displacement of the wing is a significant design consideration as large deflections lead to large stresses and increased fatigue of components cause residual stresses. The stresses in the wing panel are small compared to the yield stress of aluminum alloy. This study describes the implementation of a large wing panel, aerostructural analysis and structural parameters optimization framework that couples a three-dimensional panel method.

Keywords: wing panel, aerostructural optimization, FEM, structural analysis

Procedia PDF Downloads 576

Authors: Meziane Rachid, Boufala Seddik, Hamzi Amar, Amara Mohamed

Abstract:

Today’s energy industry seeks maximum benefit with maximum reliability. In order to achieve this goal, design engineers depend on reliability optimization techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimization method to solve the problem of wind-Solar-Gas power systems design optimization. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterized by their cost, capacity and reliability. The reliability is considered in this work as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. This definition of the reliability index is widely used for power systems. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of wind generators, transformers and lines are allowed from a list of product available in the market. Our approach has the advantage to allow electrical power components with different parameters to be allocated in electrical power systems. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and the HS algorithm. An illustrative example is presented.

Keywords: reliability optimization, harmony search optimization (HSA), universal generating function (UMGF)

Procedia PDF Downloads 565

Authors: Anna Ouskova Leonteva, Michel Risser, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

Abstract:

This paper proposes an efficient optimization tool for an active magnetic regenerator (AMR) model, operating in two modes: magnetic refrigeration system (MRS) and thermo-magnetic generator (TMG). The aim of this optimizer is to improve the design of the AMR by applying a multi-physics multi-scales numerical model as a core of evaluation functions to achieve industrial requirements for refrigeration and energy conservation systems. Based on the multi-objective non-dominated sorting genetic algorithm 3 (NSGA3), it maximizes four different objectives: efficiency and power density for MRS and TMG. The main contribution of this work is in the simultaneously application of a CPU-parallel NSGA3 version to the AMR model in both modes for studying impact of control and design parameters on the performance. The parametric study of the optimization results are presented. The main conclusion is that the common (for TMG and MRS modes) optimal parameters can be found by the proposed tool.

Keywords: ecological refrigeration systems, active magnetic regenerator, thermo-magnetic generator, multi-objective evolutionary optimization, industrial optimization problem, real-world application

Procedia PDF Downloads 100

Authors: A. Peniak, J. Makarovič, P. Rafajdus, P. Dúbravka

Abstract:

The purpose of this work is to optimize a Switched Reluctance Motor (SRM) for an automotive application, specifically for a fully electric car. A new optimization approach is proposed. This unique approach transforms automotive customer requirements into an optimization problem, based on sound knowledge of a SRM theory. The approach combines an analytical and a finite element analysis of the motor to quantify static nonlinear and dynamic performance parameters, as phase currents and motor torque maps, an output power and power losses in order to find the optimal motor as close to the reality as possible, within reasonable time. The new approach yields the optimal motor which is competitive with other types of already proposed motors for automotive applications. This distinctive approach can also be used to optimize other types of electrical motors, when parts specifically related to the SRM are adjusted accordingly.

Keywords: automotive, drive system, electric car, finite element method, hybrid car, optimization, switched reluctance motor

Procedia PDF Downloads 505

Authors: Yassir Abdelrazig, Ren Moses

Abstract:

Roadway planning and design is a very complex process involving five key phases before a project is completed; planning, project development, final design, right-of-way, and construction. The planning phase for a new roadway transportation project is a very critical phase as it greatly affects all latter phases of the project. A location study is usually performed during the preliminary planning phase in a new roadway project. The objective of the location study is to develop alignment alternatives that are cost efficient considering land acquisition and construction costs. This paper describes a methodology to develop optimal preliminary roadway alignments utilizing spatial-data. Four optimization criteria are taken into consideration; roadway length, land cost, land slope, and environmental impacts. The basic concept of the methodology is to convert the proposed project area into a grid, which represents the search space for an optimal alignment. The aforementioned optimization criteria are represented in each of the grid’s cells. A spatial-data optimization technique is utilized to find the optimal alignment in the search space based on the four optimization criteria. Two case studies for new roadway projects in Duval County in the State of Florida are presented to illustrate the methodology. The optimization output alignments are compared to the proposed Florida Department of Transportation (FDOT) alignments. The comparison is based on right-of-way costs for the alignments. For both case studies, the right-of-way costs for the developed optimal alignments were found to be significantly lower than the FDOT alignments.

Keywords: gemoetric design, optimization, planning, roadway planning, roadway design

Procedia PDF Downloads 325

Authors: Shanakr Bhattarai, V. S. Tiwari, Barak Akabayov

Abstract:

The plummeting number of infections and death is due to the development of drug-resistant bacteria. In addition, the number of approved antibiotic drugs by the Food and Drug Administration (FDA) is insufficient. Therefore, developing new drugs and finding novel targets for central metabolic pathways in bacteria is urgently needed. One of the promising targets is DNA replication machinery which consists of many essential proteins and enzymes. DnaG primase is an essential enzyme and a central part of the DNA replication machinery. DnaG primase synthesizes short RNA primers that initiate the Okazaki fragments by the lagging strand DNA polymerase. Therefore, it is reasonable to assume that inhibition of primase activity will stall DNA replication and prevent bacterial proliferation. We did the expression and purification of eight different bacterial DnaGs (Mycobacterium tuberculosis(Mtb), Bacillus anthracis (Ba), Mycobacterium smegmatis (Msmeg), Francisella tularencis (Ft), Vibrio cholerae (Vc) and Yersinia pestis (Yp), Staphylococcus aureus(Saureus), Escherichia coli(Ecoli)) followed by the radioactive activity assay. After obtaining the pure and active protein DnaG, we synthesized the inhibitors for them. The inhibitors were divided into five different groups, each containing five molecules, and the cocktail inhibition assay was performed against each DnaGs. The groups of molecules inhibiting the DnaGs were further tested with individual molecules belonging to inhibiting groups. Each molecule showing inhibition was titrated against the corresponding DnaGs to find IC50. We got a molecule(VS167) that acted as broad inhibitors, inhibiting all eight DnaGs. Molecules VS180 and VS186 inhibited seven DnaGs (except Saureus). Similarly, two molecules(VS 173, VS176) inhibited five DnaGs (Mtb, Ba, Ft, Yp, Ecoli). VS261 inhibited four DnaGs (Mtb, Ba, Ft, Vc). MS50 inhibited Ba and Vc DnaGs. And some of the inhibitors inhibited only one DnaGs. Thus we found the broad and specific inhibitors for different bacterial DnaGs, and their Structure-activity analysis(SAR) was done. Further, We tried to explain the similarities among the enzyme DnaGs from different bacteria based on their inhibition pattern.

Keywords: DNA replication, DnaG, okazaki fragments, antibiotic drugs

Procedia PDF Downloads 82

Authors: Eusebio A. Jiménez-Arévalo, Deifilia Ahuatzi-Chacón, Juvencio Galíndez-Mayer, Cleotilde Juárez-Ramírez, Nora Ruiz-Ordaz

Abstract:

Bendiocarb is a known toxic xenobiotic that presents acute and chronic risks for freshwater invertebrates and estuarine and marine biota; thus, the treatment of water contaminated with the insecticide is of concern. In this paper, a bacterial community with the capacity to grow in bendiocarb as its sole carbon and nitrogen source was isolated by enrichment techniques in batch culture, from samples of a composting plant located in the northeast of Mexico City. Eight cultivable bacteria were isolated from the microbial community, by PCR amplification of 16 rDNA; Pseudoxanthomonas spadix (NC_016147.2, 98%), Ochrobacterium anthropi (NC_009668.1, 97%), Staphylococcus capitis (NZ_CP007601.1, 99%), Bosea thiooxidans. (NZ_LMAR01000067.1, 99%), Pseudomonas denitrificans. (NC_020829.1, 99%), Agromyces sp. (NZ_LMKQ01000001.1, 98%), Bacillus thuringiensis. (NC_022873.1, 97%), Pseudomonas alkylphenolia (NZ_CP009048.1, 98%). NCBI accession numbers and percentage of similarity are indicated in parentheses. These bacteria were regarded as the isolated species for having the best similarity matches. The ability to degrade bendiocarb by the immobilized bacterial community in a packed bed biofilm reactor, using as support volcanic stone fragments (tezontle), was evaluated. The reactor system was operated in batch using mineral salts medium and 30 mg/L of bendiocarb as carbon and nitrogen source. With this system, an overall removal efficiency (η_bend) rounding 90%, was reached.

Keywords: bendiocarb, biodegradation, biofilm reactor, carbamate insecticide

Procedia PDF Downloads 259

Authors: Julie Connelly, Tanvi Toprani, Xin Xie, Dhinoth Kumar Bangarusamy, Kris C. Gunsalus

Abstract:

The overuse of antibiotics worldwide has contributed to the development of multi-drug resistant (MDR) pathogenic bacterial strains. There is an increasing urgency to discover antibiotics to combat MDR pathogens. The microbiome of the Arabian Gulf is a largely unexplored and potentially rich source of novel bioactive compounds. Microbes that inhabit the Abu Dhabi coastal regions adapt to extreme environments with high salinity, hot temperatures, large temperature fluctuations, and acute exposure to solar energy. The microbes native to this region may produce unique metabolites with therapeutic potential as antibiotics and antifungals. We have isolated 200 pure bacterial strains from mangrove sediments, cyanobacterial mats, and coral reefs of the Abu Dhabi region. In this project, we aim to screen the marine bacterial strains to identify antibiotics, in particular undocumented compounds that show activity against existing antibiotic-resistant strains. We have acquired the ESKAPE pathogen panel, which consists of six antibiotic-resistant gram-positive and gram-negative bacterial pathogens that collectively cause most clinical infections. Our initial efforts of the primary screen using colony-picking co-culture assay have identified several candidate marine strains producing potential antibiotic compounds. We will next apply different assays, including disk-diffusion and broth turbidity growth assay, to confirm the results. This will be followed by bioactivity-guided purification and characterization of target compounds from the scaled-up volume of candidate strains, including SPE fraction, HPLC fraction, LC-MS, and NMR. For antimicrobial compounds with unknown structures, our final goal is to investigate their mode of action by identifying the molecular target.

Keywords: marine bacteria, natural products, drug discovery, ESKAPE panel

Procedia PDF Downloads 63

Authors: Judith Mogouong, Philippe Constant, Robert Lavallee, Claude Guertin

Abstract:

The emerald ash borer (EAB) is an exotic wood borer insect native from China, which is associated with important environmental and economic damages in North America. Beetles are known to be vectors of microbial communities related to their adaptive capacities. It is now established that environmental stress factors may induce physiological events on the host trees, such as phytochemical changes. Consequently, that may affect the establishment comportment of herbivorous insect. Considering the number of insects collected on ash trees (insects’ density) as an abiotic factor related to stress damage, the aim of our study was to explore the dynamic of EAB gut microbial community genome (microbiome) when facing that factor and to monitor its diversity. Insects were trapped using specific green Lindgren© traps. A gradient of the captured insect population along the St. Lawrence River was used to create three levels of insects’ density (low, intermediate, and high). After dissection, total DNA extracted from insect guts of each level has been sent for amplicon sequencing of bacterial 16S rRNA gene and fungal ITS2 region. The composition of microbial communities among sample appeared largely diversified with the Simpson index significantly different across the three levels of density for bacteria. Add to that; bacteria were represented by seven phyla and twelve classes, whereas fungi were represented by two phyla and seven known classes. Using principal coordinate analysis (PCoA) based on Bray Curtis distances of 16S rRNA sequences, we observed a significant variation between the structure of the bacterial communities depending on insects’ density. Moreover, the analysis showed significant correlations between some bacterial taxa and the three classes of insects’ density. This study is the first to present a complete overview of the bacterial and fungal communities associated with the gut of EAB base on culture-independent methods, and to correlate those communities with a potential stress factor of the host trees.

Keywords: gut microbiome, DNA, 16S rRNA sequences, emerald ash borer

Procedia PDF Downloads 390

Authors: Paffrath Meinhard, Zhou Yayun, Guo Yiqing, Ertl Harald

Abstract:

Winding geometry design is an important part of power transformer electrical design. Conventionally, the winding geometry is designed manually, which is a time-consuming job because it involves many iteration steps in order to meet all cost, manufacturing and electrical requirements. Here a method is presented which automatically generates the winding geometry for given user parameters and allows the user to interactively set and change parameters. To achieve this goal, the winding problem is transferred to a mixed integer nonlinear optimization problem. The relevant geometrical design parameters are defined as optimization variables. The cost and other requirements are modeled as constraints. For the solution, a stochastic ant colony optimization algorithm is applied. It is well-known, that an optimizer can get stuck in a local minimum. For the winding problem, we present efficient strategies to come out of local minima, furthermore a reduced variable search range helps to accelerate the solution process. Numerical examples show that the optimization result is delivered within seconds such that the user can interactively change the variable search area and constraints to improve the design.

Keywords: ant colony optimization, mixed integer nonlinear programming, power transformer, winding design

Procedia PDF Downloads 369

Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

Abstract:

The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

Procedia PDF Downloads 18

Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

Abstract:

This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm

Procedia PDF Downloads 109

Authors: Bandana Saikia, Ashok Bhattacharyya

Abstract:

Endophytic microbes and their metabolites positively impact overall plant health, which may have a potential implication in agriculture. In the present study, 177 bacterial endophytes and 57 fungal endophytes were isolated, with the highest recovery rate from tomato roots. A maximum of 112 endophytes were isolated during monsoon, followed by 64 isolates and 58 isolates isolated during pre-monsoon and post-monsoon periods, respectively, indicating the rich diversity in bacterial and fungal endophytes of tomato crops from different locations of Assam, India. Further, the endophytes were evaluated for their antagonistic potential against Fusarium oxysporum f. sp. lycopersici. Fungal endophytic isolate AAUTLF (Endophytic Fungi of Tomato Leaf from Assam Agricultural University, Assam, India area) and bacterial endophyte D1B (Endophytic bacteria of tomato from Dhemiji, India district) showed the highest antifungal activity against the pathogen both in vitro and in vivo. Based on 5.8 rDNA sequence analysis of fungal and 16S rDNA sequence of bacteria endophytes, the most effective fungal and bacterial isolates against FOL were identified as Trichoderma asperellum AAUTLF and Stenotrophomonas maltophilia D1B, respectively. The isolates showed an antagonistic effect against Fusarium oxysporum f.sp. lycopersici in-vitro and reduced the disease index of Fusarium wilt in tomatoes by 64.4% under pot conditions. Trichoderma asperellum AAUTLF produced an antifungal compound viz., 6-pentyl-2H-pyran-2-one, which also possesses growth-promoting characteristics. The bacteria Stenotrophomonas maltophilia D1B produced antifungal compounds, including benzothiazole, oleic acid, phenylacetic acid, and 3-(Hydroxy-phenyl-methyl)-2,3-dimethyl-octan-4-one. This would be of high importance for the source of antagonistic strains and biocontrol of tomato Fusarium wilt, as well as other plant fungal diseases.

Keywords: root endophytes, Stemotrophomonas, Trichoderma, benzothiazole, 6-pentyl-2H-pyran-2-one

Procedia PDF Downloads 58

Authors: Sourabh Joshi, Geetinder Kaur, Sarabjit Kaur, Gulwatanpreet Singh, Geetika Mannan

Abstract:

In this paper, we have use an algorithm that able to obtain an optimal solution to travelling salesman problem from a huge search space, quickly. This algorithm is based upon the ant colony optimization technique and employees roulette wheel selection mechanism. To illustrate it more clearly, a program has been implemented which is based upon this algorithm, that presents the changing process of route iteration in a more intuitive way. In the event, we had find the optimal path between hundred cities and also calculate the distance between two cities.

Keywords: ant colony, optimization, travelling salesman problem, roulette wheel selection

Procedia PDF Downloads 432

Authors: Mohamed Abdelwahed

Abstract:

We propose an optimization algorithm for the geometric control of fluid flow. The used approach is based on the topological sensitivity analysis method. It consists in studying the variation of a cost function with respect to the insertion of a small obstacle in the domain. Some theoretical and numerical results are presented in 2D and 3D.

Keywords: sensitivity analysis, topological gradient, shape optimization, stokes equations

Procedia PDF Downloads 526

Authors: Yue Hu, Xilu Zhao, Takao Yamaguchi, Manabu Sasajima, Yoshio Koike, Akira Hara

Abstract:

This study tried optimization of design parameter of a cone loudspeaker unit as an example of the high flexibility of the products design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to each design the parameter of the loudspeaker. To overcome the limitation of the design problem in practice, this paper proposes a new an acoustic analysis algorithm to optimize design the parameter of the loudspeaker. The material character of cone paper and the loudspeaker edge was the design parameter, and the vibration displacement of the cone paper was the objective function. The results of the analysis were compared with the predicted value. They had high accuracy to the predicted value. These results suggest that, though the parameter design is difficult by experience and intuition, it can be performed comparatively easily using the optimization design by the developed acoustic analysis software.

Keywords: air viscosity, loudspeaker, cone paper, edge, optimization

Procedia PDF Downloads 390

Authors: Thomas Weber, Nina Strobel, Thomas Kohne, Eberhard Abele

Abstract:

In context of the energy transition in Germany, the importance of so-called virtual power plants in the energy supply continues to increase. The progressive dismantling of the large power plants and the ongoing construction of many new decentralized plants result in great potential for optimization through synergies between the individual plants. These potentials can be exploited by mathematical optimization algorithms to calculate the optimal application planning of decentralized power and heat generators and storage systems. This also includes linear or linear mixed integer optimization. In this paper, procedures for reducing the number of decision variables to be calculated are explained and validated. On the one hand, this includes combining n similar installation types into one aggregated unit. This aggregated unit is described by the same constraints and target function terms as a single plant. This reduces the number of decision variables per time step and the complexity of the problem to be solved by a factor of n. The exact operating mode of the individual plants can then be calculated in a second optimization in such a way that the output of the individual plants corresponds to the calculated output of the aggregated unit. Another way to reduce the number of decision variables in an optimization problem is to reduce the number of time steps to be calculated. This is useful if a high temporal resolution is not necessary for all time steps. For example, the volatility or the forecast quality of environmental parameters may justify a high or low temporal resolution of the optimization. Both approaches are examined for the resulting calculation time as well as for optimality. Several optimization models for virtual power plants (combined heat and power plants, heat storage, power storage, gas turbine) with different numbers of plants are used as a reference for the investigation of both processes with regard to calculation duration and optimality.

Keywords: CHP, Energy 4.0, energy storage, MILP, optimization, virtual power plant

Procedia PDF Downloads 160

Authors: Ersilio Tushaj

Abstract:

The efficient solution that satisfies the optimal condition is an important issue in the structural engineering design problem. The new codes of structural design consist in design methodology that looks after the exploitation of the total resources of the construction material. In recent years some non-deterministic or meta-heuristic structural optimization algorithms have been developed widely in the research community. These methods search the optimum condition starting from the simulation of a natural phenomenon, such as survival of the fittest, the immune system, swarm intelligence or the cooling process of molten metal through annealing. Among these techniques the most known are: the genetic algorithms, simulated annealing, evolution strategies, particle swarm optimization, tabu search, ant colony optimization, harmony search and big bang crunch optimization. In this study, five of these algorithms are applied for the optimum weight design of a steel truss structure with variable geometry but fixed topology. The design process selects optimum distances and size sections from a set of commercial steel profiles. In the formulation of the design problem are considered deflection limitations, buckling and allowable stress constraints. The approach is repeated starting from different initial populations. The design problem topology is taken from an existing steel structure. The optimization process helps the engineer to achieve good final solutions, avoiding the repetitive evaluation of alternative designs in a time consuming process. The algorithms used for the application, the results of the optimal solutions, the number of iterations and the minimal weight designs, will be reported in the paper. Based on these results, it would be estimated, the amount of the steel that could be saved by applying structural analysis combined with non-deterministic optimization methods.

Keywords: structural optimization, non-deterministic methods, truss structures, steel truss

Procedia PDF Downloads 216

Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

Abstract:

Keywords: structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames

Procedia PDF Downloads 357

Authors: S. Wakim, M. Nemer, B. Zeghondy, B. Ghannam, C. Bouallou

Abstract:

Designing the optimal heat exchanger is still an essential objective to be achieved. Parametrical optimization involves the evaluation of the heat exchanger dimensions to find those that best satisfy certain objectives. This method contributes to an enhanced design rather than an optimized one. On the contrary, topology optimization finds the optimal structure that satisfies the design objectives. The huge development in metal additive manufacturing allowed topology optimization to find its way into engineering applications especially in the aerospace field to optimize metal structures. Using topology optimization in 3d heat and mass transfer problems requires huge computational time, therefore coupling it with CFD simulations can reduce this it. However, existed CFD models cannot be coupled with topology optimization. The CFD model must allow creating a uniform mesh despite the initial geometry complexity and also to swap the cells from fluid to solid and vice versa. In this paper, a porous media approach compatible with topology optimization criteria is developed. It consists of modeling the fluid region of the heat exchanger as porous media having high porosity and similarly the solid region is modeled as porous media having low porosity. The switching from fluid to solid cells required by topology optimization is simply done by changing each cell porosity using a user defined function. This model is tested on a plate and fin heat exchanger and validated by comparing its results to experimental data and simulations results. Furthermore, this model is used to perform a material reallocation based on local criteria to optimize a plate and fin heat exchanger under a constant heat duty constraint. The optimized fin uses 20% fewer materials than the first while the pressure drop is reduced by about 13%.

Keywords: computational methods, finite element method, heat exchanger, porous media, topology optimization

Procedia PDF Downloads 144

Authors: Anne Elain, Magali Le Fellic

Abstract:

Halogenated Aromatic Compounds (HAC) are major organic pollutants that are detected in several environmental compartments as a result of their widespread use as solvents, pesticides and other industrial chemicals. The degradation of HAC simultaneously at low temperature and under saline conditions would be useful for remediation of polluted sites. Hence, microbial processes based on the metabolic activities of anaerobic bacteria are especially attractive from an economic and environmental point of view. Metabolites are generally less toxic, less likely to bioaccumulate and more susceptible for further degradation. Studies on biological reductive dehalogenation have largely been restricted to chlorinated compounds while relatively few have focussed on other HAC i.e., fluorinated, brominated or iodinated compounds. The objectives of the present work were to investigate the biodegradation of a mixture of triiodoaromatic molecules in industrial wastewater by an enriched bacterial consortium. Biodegradation of the mixture was studied during batch experiments in an anaerobic reactor. The degree of mineralization and recovery of halogen were monitored by HPLC-UV, TOC analysis and potentiometric titration. Providing ethanol as an electron donor was found to stimulate anaerobic reductive dehalogenation of HAC with a deiodination rate up to 12.4 mg.L-1 per day. Sodium chloride even at high concentration (10 mM) was found to have no influence on the degradation rates nor on the microbial viability. An analysis of the 16S rDNA (MicroSeq®) revealed that at least 6 bacteria were predominant in the enrichment, including Pseudomonas aeruginosa, Pseudomonas monteilii, Kocuria rhizophila, Ochrobacterium anthropi, Ralstonia pickettii and Rhizobium rhizogenes.

Keywords: halogenated aromatics, anaerobic biodegradation, deiodination, bacterial consortium

Procedia PDF Downloads 167

Authors: Yuhan Lin, Rohan Kekatpure, Cassidy Yeung

Abstract:

In Digital Marketing, robust quantification of View-through attribution (VTA) is necessary for evaluating channel effectiveness. VTA occurs when a product purchase is aided by an Ad but without an explicit click (e.g. a TV ad). A lack of a tracking mechanism makes VTA estimation challenging. Most prevalent VTA estimation techniques rely on post-purchase in-product user surveys. User surveys enable the calculation of channel multipliers, which are the ratio of the view-attributed to the click-attributed purchases of each marketing channel. Channel multipliers thus provide a way to estimate the unknown VTA for a channel from its known click attribution. In this work, we use Convex Optimization to compute channel multipliers in a way that enables a mathematical encoding of the expected channel behavior. Large fluctuations in channel attributions often result from overfitting the calculations to user surveys. Casting channel attribution as a Convex Optimization problem allows an introduction of constraints that limit such fluctuations. The result of our study is a distribution of channel multipliers across the entire marketing funnel, with important implications for marketing spend optimization. Our technique can be broadly applied to estimate Ad effectiveness in a privacy-centric world that increasingly limits user tracking.

Keywords: digital marketing, survey analysis, operational research, convex optimization, channel attribution

Procedia PDF Downloads 175

Authors: Abu Salim Mustafa

Abstract:

Water plays a major role in maintaining life on earth, but it can also serve as a matrix for pathogenic organisms, posing substantial health threats to humans. Although, outbreaks of diseases attributable to drinking water may not be common in industrialized countries, they still occur and can lead to serious acute, chronic, or sometimes fatal health consequences. The analysis of drinking water samples from different regions of Kuwait was performed in this study for bacterial and viral contaminations. Drinking tap water samples were collected from 15 different locations of the six Kuwait governorates. All samples were analyzed by confocal microscopy for the presence of bacteria. The samples were cultured in vitro to detect cultivable organisms. DNA was isolated from the cultured organisms and the identity of the bacteria was determined by sequencing the bacterial 16S rRNA genes, followed by BLAST analysis in the database of NCBI, USA. RNA was extracted from water samples and analyzed by real-time PCR for the detection of viruses with potential health risks, i.e. Astrovirus, Enterovirus, Norovirus, Rotavirus, and Hepatitis A. Confocal microscopy showed the presence of bacteria in some water samples. The 16S rRNA gene sequencing of culture grown organisms, followed by BLAST analysis, identified the presence of several non-pathogenic bacterial species. However, one sample had Acinetobacter baumannii, which often causes opportunistic infections in immunocompromised people, but none of the studied viruses could be detected in the drinking water samples analyzed. The results indicate that drinking water samples analyzed from various locations in Kuwait are relatively safe for drinking and do not contain many harmful pathogens.

Keywords: drinking water, microbial contaminant, 16S rDNA, Kuwait

Procedia PDF Downloads 140

Authors: T. Gomes, J. Manzi

Abstract:

The inclusion of stirring systems in the calculation and optimization procedures has been undergone a significant lack of attention, what it can reflect in the results because such systems provide an additional energy to the process, besides promote a better distribution of mass and energy. This is meaningful for the reactive systems, particularly for the Continuous Stirred Tank Reactor (CSTR), for which the key variables and parameters, as well as the operating conditions of stirring systems, can play a pivotal role and it has been showed in the literature that neglect these factors can lead to sub-optimal results. It is also well known that the sole use of the First Law of Thermodynamics as an optimization tool cannot yield satisfactory results, since the joint use of the First and Second Laws condensed into a procedure so-called entropy generation minimization (EGM) has shown itself able to drive the system towards better results. Therefore, the main objective of this paper is to determine the effects of key parameters of the stirring system in the optimization procedures by means of EGM applied to the reactive systems. Such considerations have been possible by dimensional analysis according to Rayleigh and Buckingham's method, which takes into account the physical and geometric parameters and the variables of the reactive system. For the simulation purpose based on the production of propylene glycol, the results have shown a significant increase in the conversion rate from 36% (not-optimized system) to 95% (optimized system) with a consequent reduction of by-products. In addition, it has been possible to establish the influence of the work of the stirrer in the optimization procedure, in which can be described as a function of the fluid viscosity and consequently of the temperature. The conclusions to be drawn also indicate that the use of the entropic analysis as optimization tool has been proved to be simple, easy to apply and requiring low computational effort.

Keywords: stirring systems, entropy, reactive system, optimization

Procedia PDF Downloads 238

Authors: Suleiman Aliyu

Abstract:

Integrating IoT applications with Fog systems presents challenges in optimization due to diverse environments and conflicting objectives. This study explores achieving Pareto optimal deployments for Fog-based IoT systems to address growing QoS demands. We introduce Pareto optimality to balance competing performance metrics. Using the FogTorch optimization framework, we propose a hybrid approach (Backtracking search with branch and bound) for scalable IoT deployments. Our research highlights the advantages of Pareto optimality over single-objective methods and emphasizes the role of FogTorch in this context. Initial results show improvements in IoT deployment cost in Fog systems, promoting resource-efficient strategies.

Keywords: pareto optimality, fog application deployment, resource allocation, internet of things

Procedia PDF Downloads 64

Authors: Debabrata Biswas

Abstract:

The current trend in the development of antibiotic resistance by multiple bacterial pathogens has resulted in a troubling loss of effective antibiotic options for human. The emergence of multi-drug-resistant pathogens has necessitated higher dosages and combinations of multiple antibiotics, further exacerbating the problem of antibiotic resistance. Zoonotic bacterial pathogens, such as Salmonella, Campylobacter, Shiga toxin-producing Escherichia coli (such as enterohaemorrhagic E. coli or EHEC), and Listeria are the most common and predominant foodborne enteric infectious agents. It was observed that these pathogens gained/developed their ability to survive in the presence of antibiotics either in farm animal gut or farm environment and researchers believe that therapeutic and sub-therapeutic antibiotic use in farm animal production might play an important role in it. The mechanism of action of antimicrobial components used in farm animal production in genomic interplay in the gut and farm environment, has not been fully characterized. Even the risk of promoting the exchange of mobile genetic elements between microbes specifically pathogens needs to be evaluated in depth, to ensure sustainable farm animal production, safety of our food and to mitigate/limit the enteric infection with multiple antibiotic resistant bacterial pathogens. Due to the consumer’s demand and considering the current emerging situation, many countries are in process to withdraw antibiotic use in farm animal production. Before withdrawing use of the sub-therapeutic antibiotic or restricting the use of therapeutic antibiotics in farm animal production, it is essential to find alternative natural antimicrobials for promoting the growth of farm animal and/or treating animal diseases. Further, it is also necessary to consider whether that compound(s) has the potential to trigger the acquisition or loss of genetic materials in zoonotic and any other bacterial pathogens. Development of alternative therapeutic and sub-therapeutic antimicrobials for farm animal production and food processing and preservation and their effective implementation for sustainable strategies for farm animal production as well as the possible risk for horizontal gene transfer in major enteric pathogens will be focus in the study.

Keywords: food safety, natural antimicrobial, sustainable farming, antibiotic resistance

Procedia PDF Downloads 256

Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

Abstract:

One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

Procedia PDF Downloads 90

Authors: Sonia Hamiche, Naima Bouzidi, Mohamed Reda Zahi, Yasmina Daghbouche, Abdelmalek Badis, Mohamed El Hattab

Abstract:

This study was carried out on the brown alga Zonaria tourfortii harvested on the central coast of Algeria. The chemical study enabled the characterization of phenolic compounds, mainly acyl phloroglucinol and chromone metabolites. The study isolated a significant quantity of all-cis-5,8,11,14,17 eicosapentanoic acid (EPA). Based on a literature review, we have proposed a biosynthetic pathway leading from EPA to phenolic metabolites. Bacterial screening from the algal surface led to isolate 30 bacterial strains, including 26 Gram+ containing the Staphylococcus and Bacillus genus, and 4 Gram- containing the Acinetobacter and Enterobacteracea genus. In terms of activity profiles, strain S13 (identified as Bacillus amyloliquefaciens based on 16S rRNA technique) proved highly interesting inhibitory activities against target germs, as well as its production of diffusible and volatile compounds. Bacterial cells from the B. amyloliquefaciens S13 strain were used to recover a volatile fraction. Analysis was carried out by gas chromatography-mass spectrometry. The main volatile compounds identified were: 13-epi-manoyl oxide (29.39%), manool (17.39%), 15,16-dinorlabd-8(20)-en-13-one (13.17%), labda-8(17),13Z-dien-15-ol (9. 51%) and 3-acetoxy-13 epimanoyl oxide (5.26%) belonging to the labdane class of diterpenes, the latter having never been described in the category of microbial volatile organic compounds. Ecological aspects were discussed.

Keywords: chemical analysis, acylphloroglucinols, phenolic compounds, microbial volatiles, Zonaria tournefortii

Procedia PDF Downloads 32