Search results for: policy optimization

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 147

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 193

Authors: Ni Tang, Jinping Zhang

Abstract:

An effective and sustainable volunteer team is needed to create a more available blood supply system. In order to promote the sustainable development of blood donation in Zhejiang Province, China, a “three-exemption” policy was proposed in 2014: blood donors who received the National Award for unpaid blood donation may government-invested and funded parks, scenic spots and other places for free, visit non-profit medical institutions for free outpatient fees, and be exempted from urban public transportation fees. As the policy has been in place for seven years, this study evaluated the effectiveness of the policy by comparing the increasing rate of blood donation in Hangzhou (capital city of Zhejiang) before and after the policy using the intermittent time series analysis. The blood donation in Anhui, a Province near Zhejiang, was also compared as a negative control. Blood donation data from 2012 to 2018 were obtained from the donation center's official websites. The increasing rate of blood donation volume since 2012 in Hangzhou is 34.37 units/month, and after 2014, the increasing rate additionally increases 71.69 (p=0.1442), which indicating a statistically non-significant change after the policy. While as a negative control, in Anhui, the increasing rate of blood donation volume since 2012 is -163.3 unit/month, and the increasing rate additionally increases 167.2 (p=5.63e-07) after 2014. The result shows that the three-exemption policy had a certain level of impact on encouraging volunteers to donate blood, but the effect was not substantial. One possible reason for the ineffectiveness of the policy might be a lack of public awareness of the policy. On the other hand, this policy mainly waived unnecessary life expenses, such as fares and scenic entrance fees, and requires a certain number of blood donations, registration procedures, and blood donation certificates. Perhaps, reducing life-related expenses such as oil, water and electricity, could better attract people to participate in blood donation. This current study on the three-exemption policy provides a new direction for promoting people's blood donation. Incentive policies may require greater publicity and incentives. In order to better ensure the operation of the blood donation system, other policies, especially incentive policies, should be further explored.

Keywords: blood donation, policy, Zhejiang, unpaid blood donation, three-exemption policy

Procedia PDF Downloads 180

Authors: Hanneke J. A. Smaling, Mandy Visser

Abstract:

One way to reduce the risk of COVID-19 infection is by preventing close interpersonal contact with distancing measures. These social distancing measures presented challenges to the health and wellbeing of people with dementia and their informal and professional caregivers. This study describes the concerns and recommendations of informal and professional caregivers for COVID-19 policy for home care and long-term care for people with dementia during the first and second COVID-19 wave in the Netherlands. In this qualitative interview study, 20 informal caregivers and 20 professional caregivers from home care services and long-term care participated. Interviews were analyzed using an inductive thematic analysis approach. Both informal and professional caregivers worried about getting infected or infecting others with COVID-19, the consequences of the distancing measures, and quality of care. There was a general agreement that policy in the second wave was better informed compared to the first wave. At an organizational level, the policy was remarkably flexible. Recommendations were given for dementia care (need to offer meaningful activities, improve the organization of care, more support for informal caregivers), policy (national vs. locally organization, social isolation measures, visitor policy), and communication. Our study contributes to the foundation of future care decisions by (inter)national policymakers, politicians, and healthcare organizations during the course of the COVID-19 pandemic, underlining the need for balance between safety and autonomy for people with dementia.

Keywords: covid-19, dementia, home care, long-term care, policy

Procedia PDF Downloads 112

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 344

Authors: Aneeq Sarwar

Abstract:

This study assesses the interaction between environmental and intellectual property policy on the rate of invention of environmental inventions and specifically tests for whether there is a synergy between stricter IP regimes and stronger environmental policies. The empirical analysis uses firm and industry-level data from seven OECD countries from 2009 to 2015. We also introduce a new measure of environmental inventions using a Natural Language Processing Topic Modelling technique. We find that intellectual property policy strictness demonstrates greater effectiveness in encouraging inventiveness in environmental inventions when used in combination with stronger environmental policies. This study contributes to existing literature in two ways. First, it devises a method for better identification of environmental technologies, we demonstrate how our method is more comprehensive than existing methods as we are better able to identify not only environmental inventions, but also major components of said inventions. Second, we test how various policy regimes affect the development of environmental technologies, we are the first study to examine the interaction of the environmental and intellectual property policy on firm level innovation.

Keywords: environmental economics, economics of innovation, environmental policy, firm level

Procedia PDF Downloads 132

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 132

Authors: Chueh Chi-An, Chan Hui-Ya

Abstract:

Background and Aim: National Health Insurance (NHI) Administration released a new reimbursement policy for hospital patients who received a superior discharge plan on April 1, 2016. Each case could be claimed 1,500 points for fee-of service with related documents. The policy is considered a solution to help reducing the crowding in the emergency department, the length of stay of hospital, unplanned readmission rate and unplanned ER visit. This study aim is to explore the effect of the new reimbursement policy for discharge planning service in a medical center. Methods: The discharge team explained to general wards the new policy and encouraged early assessment, communication and connecting to community care for patients. They stated the benefit from the policy and asked documenting for reimbursement claiming from April to May 2016. The imbursement fee of NHI declaration from June 2015 to October 2017 was collected. The indicators included hospital occupancy rate, hospital bed turnover rate, long-term hospitalization rate, and patients’ satisfaction were analyzed after the policy implemented. Results: The results showed that the amount of service declaration was increasing from 2 cases in February 2016 to 110 cases in October 2017, the application rate was increasing from 0.029% to 1.576% of all inpatient cases, and the average payment from NHI was around 148,500 NT dollars per month in 2017. There are no significant differences in the indicators among hospital occupancy rate, hospital bed turnover rate, long-term hospitalization rate, and patients’ satisfaction. Conclusion: To provide a good discharge plan require a specialized case manager, the new reimbursement policy is too complicated and the total fee-of-service hospital could claim is too limited to hiring one. The results suggest more strategies combine with the new reimbursement policy will be needed.

Keywords: discharge planning, reimbursement, unplanned ER visit, readmission rate

Procedia PDF Downloads 151

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 144

Authors: Wiem Zemzem, Moncef Tagina

Abstract:

In this work, we are addressing the problem of an autonomous mobile robot navigating in a large, unknown and dynamic environment using reinforcement learning abilities. This problem is principally related to the exploration/exploitation dilemma, especially the need to find a solution letting the robot detect the environmental change and also learn in order to adapt to the new environmental form without ignoring knowledge already acquired. Firstly, a new action selection strategy, called ε-greedy-MPA (the ε-greedy policy favoring the most promising actions) is proposed. Unlike existing exploration/exploitation policies (EEPs) such as ε-greedy and Boltzmann, the new EEP doesn’t only rely on the information of the actual state but also uses those of the eventual next states. Secondly, as the environment is large, an exploration favoring least recently visited states is added to the proposed EEP in order to accelerate learning. Finally, various simulations with ball-catching problem have been conducted to evaluate the ε-greedy-MPA policy. The results of simulated experiments show that combining this policy with the Qlearning method is more effective and efficient compared with the ε-greedy policy in stationary environments and the utility-based reinforcement learning approach in non stationary environments.

Keywords: autonomous mobile robot, exploration/ exploitation policy, large, dynamic environment, reinforcement learning

Procedia PDF Downloads 389

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 228

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 49

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 69

Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran

Abstract:

In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.

Keywords: parameterization, response surface, structure optimization, pressure hull

Procedia PDF Downloads 206

Authors: Aouaouche Elmaouhab, Hassina Beggar

Abstract:

Solving multi-objective discrete optimization applications has always been limited by the resources of one machine: By computing power or by memory, most often both. To speed up the calculations, the grid computing represents a primary solution for the treatment of these applications through the parallelization of these resolution methods. In this work, we are interested in the study of some methods for solving multiple objective integer linear programming problem based on Branch-and-Bound and the study of grid computing technology. This study allowed us to propose an implementation of the method of Abbas and Al on the grid by reducing the execution time. To enhance our contribution, the main results are presented.

Keywords: multi-objective optimization, integer linear programming, grid computing, parallel computing

Procedia PDF Downloads 457

Authors: Zahid Ullah, Atlas Khan

Abstract:

This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

Procedia PDF Downloads 86

Authors: Gulnaz Erkomaishvili, Eteri Kharaishvili, Marina Chavleishvili

Abstract:

The paper discusses the economic policy of increasing national competitiveness, the tools, and means which help the country to improve its competitiveness. The sectors of the economy, in which the country can achieve a competitive advantage, are studied. It is noted that the country’s economic policy plays an important role in obtaining and maintaining a competitive advantage - authority should take measures to ensure a high level of education; scientific and research activities should be funded by the state; foreign direct investments should be attracted mainly in science-intensive industries; adaptation with the latest scientific achievements of the modern world and deepening of scientific and technical cooperation. Stable business environment and export-oriented strategy is the basis for the country’s economic growth. The studies have shown that institutional reforms in Georgia are not enough to significantly improve the country's competitiveness.

Keywords: competitiveness, economic policy, competitiveness improvement strategy, competitiveness of Georgia

Procedia PDF Downloads 107

Authors: Bale Baidi Blaise, Gilles Marckmann, Liman Kaoye, Talaka Dya, Moustapha Bachirou, Gambo Betchewe, Tibi Beda

Abstract:

This work highlights the capabilities of particles swarm optimization (PSO) method to identify parameters of hyperelastic models. The study compares this method with Genetic Algorithm (GA) method, Least Squares (LS) method, Pattern Search Algorithm (PSA) method, Beda-Chevalier (BC) method and the Levenberg-Marquardt (LM) method. Four classic hyperelastic models are used to test the different methods through parameters identification. Then, the study compares the ability of these models to reproduce experimental Treloar data in simple tension, biaxial tension and pure shear.

Keywords: particle swarm optimization, identification, hyperelastic, model

Procedia PDF Downloads 142

Authors: Chee Sun Won

Abstract:

This paper presents the size reduction of images for machine-to-machine communications. Here, the salient image regions to be preserved include the image patches of the key-points such as corners and blobs. Based on a saliency image map from the key-points and their image patches, an axis-aligned grid-size optimization is proposed for the reduction of image size. To increase the size-reduction efficiency the aspect ratio constraint is relaxed in the constraint optimization framework. The proposed method yields higher matching accuracy after the size reduction than the conventional content-aware image size-reduction methods.

Keywords: image compression, image matching, key-point detection and description, machine-to-machine communication

Procedia PDF Downloads 390

Authors: Shabnam Dadparvar, Laijin Shen

Abstract:

This paper focuses on China’s policy in the Middle East and the rivalry with the U.S. The question is that what are the main factors on China’s Middle East policy and its competition with the U.S? The hypothesis regards to three effective factors: 'China’s energy dependency' on the Middle East, 'economy' and support for 'stability' in the Middle East. What is important in China’s competition with the U.S regarding to its Middle East policy is the substantial difference in ways of treating the countries of the region; China is committed to Westphalia model based on non-interference in internal affairs of the countries and respect the sovereignty of the governments. However, after 9/11, the U.S is seeking a balance between stability and change through intervention in the international affairs and in some cases is looking for a regime change. From the other hand, China, due to its dependency on the region’s energy welcomes America’s military presence in the region for providing stability. The authors by using a descriptive analytical method try to explain the situation of rivalry between China and the United States in Middle East. China is an 'emerging power' with high economic growth and in demand of more energy supply. The problem is that a rising power in the region is often a source of concern for hegemony.

Keywords: China's foreign policy, energy, hegemony, the Middle East

Procedia PDF Downloads 321

Authors: Dimitrios J. Dimitriou

Abstract:

Considering a worldwide tendency, air transports are growing very fast and many changes have taken place in planning, management and decision making process. Given the complexity of airport operation, the best use of existing capacity is the key driver of efficiency and productivity. This paper deals with the evaluation framework for the ground access at airports, by using a set of mode choice indicators providing key messages towards airport’s ground access performance. The application presents results for a sample of 12 European airports, illustrating recommendations to define policy and improve service for the air transport access chain.

Keywords: airport ground access, air transport chain, airport access performance, airport policy

Procedia PDF Downloads 349

Authors: Fabien Candau, Florian Lafferrere

Abstract:

This article analyses how an emission policy for vessels (named California’s Ocean-Going Vessel Fuel Rule) was implemented in 2009 in California impacts trade and marine biodiversity. By studying the decrease in emission levels anticipated by the policy, we measure not only the consequences for port activities but also for one of the most important marine ecosystems of the California Coast: the Kelp forests. Using the Difference in Difference (DiD) approach at the Californian ports level, we find that this policy has led to a significant decrease in trade volume during this period. Therefore, we find a positive and significant effect of shipping policy on kelp canopy and biomass growth by controlling the specific climatic and environmental characteristics of California coastal areas.

Keywords: international trade, shipping, marine biodiversity, emission control area

Procedia PDF Downloads 42

Authors: Muhammad Zaman, Guohua Fang, Muhammad Saifullah,

Abstract:

The presented study emphasize at an optimal model to compare past and future optimal hydropower generation. In order to get maximum benefits from the Xin’anjiang hydropower station a model is developed. A Particle Swarm Optimization (PSO) has purposed and past and future water flow is used to get the maximum benefits from future water resources in this study. The results revealed that the future hydropower generation is more than the past generation. This paper gives us idea that what could we get in the past using optimal method of electricity generation and what can we get in the future using this technique.

Keywords: PSO, future water resources, optimization, Xin’anjiang,

Procedia PDF Downloads 410

Authors: Mandeep Kumar, Hari Singh

Abstract:

The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents the optimization aspects of a Wire Electrical Discharge Machining operation using Inconel X-750 as work material. The objective considered in this study is minimization of the dimensional deviation. Six input process parameters of WEDM namely spark gap voltage, pulse-on time, pulse-off time, wire feed rate, peak current and wire tension, were chosen as variables to study the process performance. Taguchi's design of experiments methodology has been used for planning and designing the experiments. The analysis of variance was carried out for raw data as well as for signal to noise ratio. Four input parameters and one two-factor interaction have been found to be statistically significant for their effects on the response of interest. The confirmation experiments were also performed for validating the predicted results.

Keywords: ANOVA, DOE, inconel, machining, optimization

Procedia PDF Downloads 176

Authors: Yehya Abdellatif, Iyad M. Muslih, Azzah Alkhalailah, Abdallah Muslih

Abstract:

Hybrid Optimization Model for Electric Renewable (HOMER) software was utilized to find the optimum design of a hybrid off-Grid system, by choosing the optimal solution depending on the cost analysis of energy based on different capacity shortage percentages. A complete study for the site conditions and load profile was done to optimize the design and implementation of a hybrid off-grid power station. In addition, the solution takes into consecration the ambient temperature effect on the efficiency of the power generation and the economical aspects of selection depending on real market price. From the analysis of the HOMER model results, the optimum hybrid power station was suggested, based on wind speed, and solar conditions. The optimization function objective is to minimize the Net Price Cost (NPC) and the Cost of Energy (COE) with zero and 10 percentage of capacity shortage.

Keywords: energy modeling, HOMER, off-grid system, optimization

Procedia PDF Downloads 546

Authors: Cameron Jones

Abstract:

Emissions, produced by landfill waste from demolished obsolete buildings, have a damaging effect on both the Earth’s climate and human health. The philosophical theory of material vitalism - the potential for materials to react and emit harmful pollutants - therefore defines this construction and demolition waste (CDW) as having vitality. The European Union’s ‘Circular Economic Action Plan’ (CEAP) aims to mitigate the effects of CDW by prioritising the circularity of building materials. This dissertation examines how the philosophical theory of material vitalism can make an environmentally responsible contribution to CDW policy. The CEAP and Silvertown Quays development are used as case studies for the application of vitalism to policy revision. The study concludes that vitalism has a positive role to play in informing CDW policy, although its contribution is stronger in some areas. This is established by first appraising the aspects that relate to the obsolescence of buildings outlined in the EU’s existing CDW policies. Next, these policy directives are compared with the CE principles employed in the Silvertown Quays development. Subsequently, a keyword analysis model is used to categorise the language used in the CEAP, demonstrating how socio-political approaches to the CE and strategies to address resource scarcity could be strengthened to represent the EU’s policy aspirations more effectively. Recommendations are then made on how material vitalism could be utilised to strengthen legislation, arguing that a notable contribution can be made in most policy areas. Finally, theoretical testing of the impact of these revisions to policy on the case study development identified some practicalities for consideration in improving waste management outcomes.

Keywords: vitalism, construction waste, obsolescence, political ecology, exceptionalism

Procedia PDF Downloads 24

Authors: Muhammad Umar Kiani, Muhammad Shahbaz

Abstract:

Performance of any vehicle can be predicted by its design/modeling and optimization. Design optimization leads to efficient performance. Followed by horizontal launch, the air launch re-entry vehicle undergoes a launch maneuver by introducing a carefully selected angle of attack profile. This angle of attack profile is the basic element to complete a specified mission. Flight program of said vehicle is optimized under the constraints of the maximum allowed angle of attack, lateral and axial loads and with the objective of reaching maximum altitude. The main focus of this study is the endo-atmospheric phase of the ascent trajectory. A three degrees of freedom trajectory model is simulated in MATLAB. The optimization process uses evolutionary algorithm, because of its robustness and efficient capacity to explore the design space in search of the global optimum. Evolutionary Algorithm based trajectory optimization also offers the added benefit of being a generalized method that may work with continuous, discontinuous, linear, and non-linear performance matrix. It also eliminates the requirement of a starting solution. Optimization is particularly beneficial to achieve maximum advantage without increasing the computational cost and affecting the output of the system. For the case of launch vehicles we are immensely anxious to achieve maximum performance and efficiency under different constraints. In a launch vehicle, flight program means the prescribed variation of vehicle pitching angle during the flight which has substantial influence reachable altitude and accuracy of orbit insertion and aerodynamic loading. Results reveal that the angle of attack profile significantly affects the performance of the vehicle.

Keywords: endo-atmospheric, evolutionary algorithm, efficient performance, optimization process

Procedia PDF Downloads 386

Authors: Shabana Noureen, Zafar Mahmood

Abstract:

Economies consistently engage in trade across borders and face tariff, non-tariff barriers and other quotas that constitute trade costs. The trade costs imposed by policy barriers on exports are considered an impediment in the export growth rate. This work aims to measure over-year trends in total and bilateral trade costs and their trends in relevance to policy barriers (tariff and non-tariff). The analysis through the micro-founded theoretically based gravity model showed that the total trade costs have a general decreasing trend in the world while in the case of developing countries, the rate by which these trends decline is very low. Bilateral trade cost estimates associated with the policy barriers represent that the non-tariff barriers in a developing country have a major role in sustaining the high trade costs as compared to the tariff barriers. This ultimately leads to a low net declining rate. This work emphasizes that for developing countries the non-tariff barriers are a major factor that renders their exports and to be uncompetitive in the world market.

Keywords: trade costs, policy barriers, tariff barriers, non-tariff barriers, trade policies, export growth

Procedia PDF Downloads 226

Authors: Mehdi Jalalpour, Mazdak Tootkaboni

Abstract:

We present a computationally efficient method for reliability-based topology optimization under material properties uncertainty, which is assumed to be lognormally distributed and correlated within the domain. Computational efficiency is achieved through estimating the response statistics with stochastic perturbation of second order, using these statistics to fit an appropriate distribution that follows the empirical distribution of the response, and employing an efficient gradient-based optimizer. The proposed algorithm is utilized for design of new structures and the changes in the optimized topology is discussed for various levels of target reliability and correlation strength. Predictions were verified thorough comparison with results obtained using Monte Carlo simulation.

Keywords: material uncertainty, stochastic perturbation, structural reliability, topology optimization

Procedia PDF Downloads 574

Authors: Bandar A. Alkhayyal

Abstract:

Modern supply chains are typically linear, transforming virgin raw materials into products for end consumers, who then discard them after use to landfills or incinerators. Nowadays, there are major efforts underway to create a circular economy to reduce non-renewable resource use and waste. One important aspect of these efforts is the development of Green Supply Chain (GSC) systems which enables a reverse flow of used products from consumers back to manufacturers, where they can be refurbished or remanufactured, to both economic and environmental benefit. This paper develops novel multi-objective optimization models to inform GSC system design at multiple levels: (1) strategic planning of facility location and transportation logistics; (2) tactical planning of optimal pricing; and (3) policy planning to account for potential valuation of GSC emissions. First, physical linear programming was applied to evaluate GSC facility placement by determining the quantities of end-of-life products for transport from candidate collection centers to remanufacturing facilities while satisfying cost and capacity criteria. Second, disassembly and remanufacturing processes have received little attention in industrial engineering and process cost modeling literature. The increasing scale of remanufacturing operations, worth nearly $50 billion annually in the United States alone, have made GSC pricing an important subject of research. A non-linear physical programming model for optimization of pricing policy for remanufactured products that maximizes total profit and minimizes product recovery costs were examined and solved. Finally, a deterministic equilibrium model was used to determine the effects of internalizing a cost of GSC greenhouse gas (GHG) emissions into optimization models. Changes in optimal facility use, transportation logistics, and pricing/profit margins were all investigated against a variable cost of carbon, using case study system created based on actual data from sites in the Boston area. As carbon costs increase, the optimal GSC system undergoes several distinct shifts in topology as it seeks new cost-minimal configurations. A comprehensive study of quantitative evaluation and performance of the model has been done using orthogonal arrays. Results were compared to top-down estimates from economic input-output life cycle assessment (EIO-LCA) models, to contrast remanufacturing GHG emission quantities with those from original equipment manufacturing operations. Introducing a carbon cost of $40/t CO2e increases modeled remanufacturing costs by 2.7% but also increases original equipment costs by 2.3%. The assembled work advances the theoretical modeling of optimal GSC systems and presents a rare case study of remanufactured appliances.

Keywords: circular economy, extended producer responsibility, greenhouse gas emissions, industrial ecology, low carbon logistics, green supply chains

Procedia PDF Downloads 139