scholarly journals LAND-USE PLANNING FOR SUSTAINABLE URBAN DEVELOPMENT IN AFRICA: A SPATIAL AND MULTI-OBJECTIVE OPTIMIZATION APPROACH

2019 ◽  
Vol 45 (5) ◽  
pp. 1-15 ◽  
Author(s):  
Alex Lubida ◽  
Mozafar Veysipanah ◽  
Petter Pilesjo ◽  
Ali Mansourian
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Geographical Information ◽  
Sustainable Urban Development ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
The City

Land-use planning, which requires finding a balance among different conflicting social, economic and environment factors, is a complex task needed everywhere, including Africa. One example is the city of Zanzibar in Tanzania, which is under special consideration for land-use revision. From one side, the city has high potentials for tourist industry and at the other side there are major challenges with the city structure and poor accessibilities. In order to prepare a proper land-use plan for the city, a variety of influencing conflicting factors needs to be considered and satisfied. This can be regarded as a common problem in many African cities, which are under development. This paper aims to address the problem by proposing and demonstrating the use of Geographical Information System (GIS) and multi-objective optimization for land-use planning, in Zanzibar as a case study. The measures which have been taken by Zanzibar government to address the development challenges through the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) were identified by studying related documents and interviewing experts. Based on these, two objective functions were developed for land-use planning. Optimum base land-use plans were developed and mapped by optimizing the objective functions using the NSGA-II algorithm. The results show that the proposed approach and outputs can considerably facilitate land-use planning in Zanzibar. Similar approaches are highly recommended for other cities in Africa which are under development.

Spatial Multi-Objective Optimization Approach for Land Use Allocation Using NSGA-II

2014 ◽  
Vol 7 (3) ◽  
pp. 906-916 ◽  
Author(s):  
Mehran Shaygan ◽  
Abbas Alimohammadi ◽  
Ali Mansourian ◽  
Zohreh Shams Govara ◽  
S. Mostapha Kalami
Keyword(s):  
Land Use ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Land Use Allocation

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Design of Optimal Operational Parameters for Steam-Alternating-Solvent Processes in Heterogeneous Reservoirs - A Multi-Objective Optimization Approach

2021 ◽  
Author(s):  
Israel Mayo-Molina ◽  
Juliana Y. Leung
Keyword(s):  
Sensitivity Analysis ◽  
Computational Time ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective ◽  
Bottom Hole ◽  
Decision Variables ◽  
Conflicting Objectives

Abstract The Steam Alternating Solvent (SAS) process has been proposed and studied in recent years as a new auspicious alternative to the conventional thermal (steam-based) bitumen recovery process. The SAS process incorporates steam and solvent (e.g. propane) cycles injected alternatively using the same configuration as the Steam-Assisted Gravity-Drainage (SAGD) process. The SAS process offers many advantages, including lower capital and operational cost, as well as a reduction in water usage and lower Greenhouse Gas (GHG) Emissions. On the other hand, one of the main challenges of this relatively new process is the influence of uncertain reservoir heterogeneity distribution, such as shale barriers, on production behaviour. Many complex physical mechanisms, including heat transfer, fluid flows, and mass transfer, must be coupled. A proper design and selection of the operational parameters must consider several conflicting objectives. This work aims to develop a hybrid multi-objective optimization (MOO) framework for determining a set of Pareto-optimal SAS operational parameters under a variety of heterogeneity scenarios. First, a 2-D homogeneous reservoir model is constructed based on typical Cold lake reservoir properties in Alberta, Canada. The homogeneous model is used to establish a base scenario. Second, different shale barrier configurations with varying proportions, lengths, and locations are incorporated. Third, a detailed sensitivity analysis is performed to determine the most impactful parameters or decision variables. Based on the results of the sensitivity analysis, several objective functions are formulated (e.g., minimizing energy and solvent usage). Fourth, Response Surface Methodology (RSM) is applied to generate a set of proxy models to approximate the non-linear relationship between the decision variables and the objective functions and to reduce the overall computational time. Finally, three Multi-Objective Evolutionary Algorithms (MOEAs) are applied to search and compare the optimal sets of decision parameters. The study showed that the SAS process is sensitive to the shale barrier distribution, and that impact is strongly dependent on the location and length of a specific shale barrier. When a shale barrier is located near the injector well, pressure and temperature may build up in the near-well area, preventing additional steam and solvent be injected and, consequently, reducing the oil production. Operational constraints, such as bottom-hole pressure, steam trap criterion, and bottom-hole gas rate in the producer, are among various critical decision variables examined in this study. A key conclusion is that the optimal operating strategy should depend on the underlying heterogeneity. Although this notion has been alluded to in other previous steam- or solvent-based studies, this paper is the first to utilize a MOO framework for systematically determining a specific optimal strategy for each heterogeneity scenario. With the advancement of continuous downhole fibre-optic monitoring, the outcomes can potentially be integrated into other real-time reservoir characterization and optimization work-flows.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM

2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Steel Structures ◽  
American Concrete Institute ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Pareto Fronts

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.

Multi-objective Optimization of Preparation Conditions of Asymmetric Polyetherimide Membrane for Prevaporation of Isopropanol

2016 ◽  
Vol 11 (1) ◽  
pp. 47-50 ◽  
Author(s):  
Seyed Reza Nabavi
Keyword(s):  
Separation Factor ◽  
Polymer Concentration ◽  
Membrane Preparation ◽  
Minor Amount ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Preparation Conditions ◽  
Membrane Flux

Abstract Multi-objective optimization is used in many chemical engineering fields that have conflict objective functions. Prevaporation is an effective process for removing trace or minor amount of the component of diluting solutions. This process is used for dehydration of alcohols containing small amounts of water. In this process membrane flux and separation factor have conflict with each other. So a multi-objective optimization approach can be used for optimization of the process. In this paper, in first stage a neural network based model was developed for preparation conditions for polyetherimide membrane in isopropanol prevaporation. Four major variables involved in the membrane preparation procedure, including polymer concentration, additive content, solvent evaporation temperature and time was considered. Membrane flux and separation factor were considered as objective functions. Elitist Non-dominated sorting genetic algorithm with jumping gene and altruistic adaptation (Alt-NSGA-aJG) was applied for simultaneous maximization of flux and separation factor. Pareto optimal solutions for membrane preparation conditions and effect of decision variables (four preparation variables) on Pareto front were investigated.

Multi-Objective Optimization of Modified Cycloidal-Toothed Gerotor Pumps by Genetic Algorithm

2019 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Contact Stress ◽  
Performance Metrics ◽  
Radius Of Curvature ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Flow Ripple ◽  
Multi Objective ◽  
Profile Type ◽  
Radial Gap

Abstract Cycloidal-toothed gerotors are formed by the combination of epicycloid and a hypocyloid arcs that use the pitch circles as their base circles. They are a common profile type used in industry likely because they can be generated by simple parametric equations. One of the problems with the cycloidal-toothed profile type is that the radius of curvature of both the inner and outer gear are zero when the gears contact at the pitch point which can lead to high contact stress. A gear generation algorithm has been developed that modifies the hypocycloid tooth form to eliminate contact in regions with very low radii of curvature that is yet to be described in scientific literature. Seven performance metrics are developed to evaluate size, flow ripple, adhesive wear, contact stress, radial gap leakage, lateral gap leakage, and inlet throttling that are used as objective functions in a multi-objective optimization. The pump geometry is optimized by applying the NSGA-II algorithm with a population size of 1000 for 500 generations to produce a pareto front, and the results are compared to two cycloidal-toothed gerotors used in the automotive industry. Two designs were found that significantly reduce the contact stress in the profiles while giving the same performance in the other six objective functions in comparison to the profiles used in industry. Another two designs are found that can significantly reduce several objective functions if the size of the pump can be increased slightly.

scholarly journals Multi-Objective Optimization of Friction Stir Spot-Welded Parameters on Aluminum Alloy Sheets Based on Automotive Joint Loads

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 520 ◽  
Author(s):  
Biao Zhang ◽  
Xin Chen ◽  
Kaixuan Pan ◽  
Jianing Wang
Keyword(s):  
Axial Stress ◽  
Peel Strength ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Friction Stir ◽  
Multi Objective ◽  
Load Mode ◽  
Weld Parameters ◽  
Spot Welded

By controlling various friction stir spot-welded (FSSW) factors, two base sheets AA 5052-H32 and 6061-T6 were selected to bond similar and dissimilar metal joints while considering dissimilar configuration orders. The effects of weld parameters on the sheer strength and peel strength were separately developed into empirical models utilizing the integrated central composite matrix design and response surface methodology (RSM). Meanwhile, the finite element (FE) analysis of the multi-axis load-bearing characteristics for automotive solder joints during service was carried out. As a result, the weights of the shear and axial stress, accounting for 90.5% and 9.5% respectively, were employed to restrict the relationship between multiple target properties, and the resulting security strength was applied to determine the feasible domain in subsequent parametric optimization. In order to enable the optimal multi-axis capacities in accordance with the load mode, the genetic algorithm NSGA-II was chosen to compute the Pareto front and further determine the best compromise solutions. The obtained optimums corresponding to each joining condition were validated by confirmation runs, indicating that this coupled multi-objective optimization approach based on working conditions was beneficial to the targeted improvement of post-weld mechanical properties.

scholarly journals The Risk Atlas of Mexico City, Mexico: A Tool for Decision-Making and Disaster Prevention

2021 ◽  
Author(s):  
David A. Novelo-Casanova ◽  
Gerardo Suárez ◽  
Enrique Cabral-Cano ◽  
Enrique A. Fernández-Torres ◽  
Oscar A. Fuentes-Mariles ◽  
...  
Keyword(s):  
Land Use ◽  
Mexico City ◽  
Land Use Planning ◽  
Forest Fires ◽  
High Probability ◽  
Volcanic Eruptions ◽  
Geographical Information ◽  
Disaster Prevention ◽  
The Impact ◽  
The City

Abstract We present a Geographical-Information-System-based Risk Atlas (GIS-RA) of Mexico City, Mexico. The main purposes are to provide a local-government level tool for implementing preventive and remedial actions useful for land-use development plans and to strengthen the culture for disaster prevention. We identified the prevalent social risk to the more relevant hazards in Mexico City (CDMX): earthquakes, volcanic eruptions, floods, landslides, forest fires, and land subsidence. A total of 274 shape-file maps were generated in this project. Seismic hazard was estimated for return periods (RP) of 20, 125, 250, and 475 years. Three areas in central and northwestern CDMX were identified along the Younger Chichinautzin Monogenetic Volcanic Field with high probability of forming a new volcano. Subsidence is concentrated to the east and southeast of CDMX, where subsidence rates are among the highest worldwide. Flooding events were estimated for RP of 2, 5, 10, 50 and 100 years and the majority of them are concentrated in the central and northern sectors of the city. During the dry season (December-April), southern CDMX has very high probability of forest fire occurrence. There is high susceptibility of landslides on the west and southwest of the city. The goals of this GIS-RA are to: 1) improve the knowledge of the potential impact of local hazards; 2) provide elements for disaster prevention, mitigation, preparedness, and response; 3) benefit decision-makers with robust risk data; 4) provide information for land-use planning; and 5) support further research to reduce the impact of disasters.

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