지속 가능한 화학 공정 설계를 위한 리스크 기반의 배치 최적화에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2013. 8. 윤인섭.This thesis presents the method and applications of process layout optimization based on the quantitative assessment of the individual risk (IR) in order to limit the effect to humans from the accidents can occur in a chemical process. The process layout of chemical plants is usually designed in a compact configuration for economic efficiency, although most of the chemical process units operate under high pressure and temperature, and/or deal with hazardous materials which are flammable or toxic. The possibility of the accident such as fires, explosions, and toxic gas releases which can cause severe damage to humans and properties is always present, and the social concerns of the community for this are also accompanied. Therefore, a method to quantitatively evaluate the risks arise from the chemical process equipment/facilities is required so that the actual damage can be prevented. This study tries to achieve such goal by proper arrangement of the process layout. First, various former approaches for the process layout problem, their formulations, and the solution methods have been analyzed. In addition, the method of quantitative risk assessment (QRA) of chemical processes and the concept of risk indices are introduced. Subsequently, the formulation of the risk-based layout optimization problem for sustainable chemical process design is presented. The individual risks (IR) caused from the fire and explosion that can affect the workers in the process site and the surrounding public are calculated according to the distance from the equipment, and then converted into the safety distance. The risk zones around the process equipment are modeled by using the safety distance constraints and the former layout optimization problems. Then the costs of process layout including land, pipeline, equipment purchase and protective devices are minimized to determine the economically optimized process layout. The formulation of layout optimization problem uses the framework of mixed-integer linear programming (MILP), and the procedure of iterative search for the reduced problem is applied to tackle the problem with large scale. Process layout optimization based on individual risk (IR) through these procedures can provide the layout that secures the inherent safety as well as the economic feasibility. The proposed methodology is applied to three kinds of chemical processes for validation. First case is dimethyl ether (DME) filling stationan example of the fuel gas station which is the simplest process but can cause heavy damage to humans due to its typical location. Next application is an ethylene oxide (EO) plant, as an example of general chemical process plant. In that case, the selection among the options for site location with different surrounding land uses is considered. A liquefaction process of an LNG-FPSO (liquefied natural gas - floating production, storage and offloading) vessel is considered last for multi-floor and more space-restricted case. Through these case studies, it has been shown that the proposed method can enhance the sustainability of the process layout by ensuring the safety and support the decision making related to the process layout in the early stage of process design.Abstract i Table of Contents v 1 Introduction 1 1.1 Motivation 4 1.2 Research scope 5 1.3 Thesis outline 6 2 Backgrounds Theory 9 2.1 Process layout optimization 9 2.1.1 Heuristic models 9 2.1.2 Mathematical models 12 2.2 Quantitative risk assessment 16 2.2.1 Risk indices 16 2.2.2 Assessment of risks 21 3 Risk-based Process Layout Optimization 29 3.1 Individual risk assessment and safety distances 31 3.2 Mathematical formulation for layout problem 34 3.2.1 Objective function 35 3.2.2 Risk Zone constraints 37 3.2.3 Other constraints 47 3.3 Iterative search for efficient solution 47 4 Case Studies 51 4.1 Facility layout optimization of DME filling station 51 4.1.1 Problem statement 53 4.1.2 Risk calculation 59 4.1.3 Layout result and discussion 62 4.2 Optimal layout of ethylene oxide plant 80 4.2.1 Problem statement 81 4.2.2 Risk calculation 88 4.2.3 Layout result and discussion 90 4.3 Multi-floor layout optimization of liquefaction process of LNG FPSO 105 4.3.1 Problem statement 108 4.3.2 Formulation for multi-floor layout 112 4.3.3 Risk calculation 115 4.3.4 Layout result and discussion 119 5 Conclusion 127 Nomenclatures 131 References 135 초록Docto

最小k部分木問題に対する生物規範型ハイブリッドメタ戦略に基づく近似解法の研究

広島大学(Hiroshima University)博士(工学)Engineeringdoctora

Generating cutting planes through inequality merging on multiple variables in knapsack problems

Master of ScienceIndustrial & Manufacturing Systems EngineeringTodd W. EastonInteger programming is a field of mathematical optimization that has applications across a wide variety of industries and fields including business, government, health care and military. A commonly studied integer program is the knapsack problem, which has applications including project and portfolio selection, production planning, inventory problems, profit maximization applications and machine scheduling. Integer programs are computationally difficult and currently require exponential effort to solve. Adding cutting planes is a way of reducing the solving time of integer programs. These cutting planes eliminate linear relaxation space. The theoretically strongest cutting planes are facet defining inequalities. This thesis introduces a new class of cutting planes called multiple variable merging cover inequalities (MVMCI). The thesis presents the multiple variable merging cover algorithm (MVMCA), which runs in linear time and produces a valid MVMCI. Under certain conditions, an MVMCI can be shown to be a facet defining inequality. An example demonstrates these advancements and is used to prove that MVMCIs could not be identified by any existing techniques. A small computational study compares the computational impact of including MVMCIs. The study shows that finding an MVMCI is extremely fast, less than .01 seconds. Furthermore, including an MVMCI improved the solution time required by CPLEX, a commercial integer programming solver, by 6.3% on average

Adaptable Spatial Agent-Based Facility Location for Healthcare Coverage

Lack of access to healthcare is responsible for the world’s poverty, mortality and morbidity. Public healthcare facilities (HCFs) are expected to be located such that they can be reached within reasonable distances of the patients’ locations, while at the same time providing complete service coverage. However, complete service coverage is generally hampered by resource availability. Therefore, the Maximal Covering Location Problem (MCLP), seeks to locate HCFs such that as much population as possible is covered within a desired service distance. A consideration to the population not covered introduces a distance constraint that is greater than the desired service distance, beyond which no population should be. Existing approaches to the MCLP exogenously set the number of HCFs and the distance parameters, with further assumption of equal access to HCFs, infinite or equal capacity of HCFs and data availability. These models tackle the real-world system as static and do not address its intrinsic complexity that is characterised by unstable and diverse geographic, demographic and socio-economic factors that influence the spatial distribution of population and HCFs, resource management, the number of HCFs and proximity to HCFs. Static analysis incurs more expenditure in the analytical and decision-making process for every additional complexity and heterogeneity. This thesis is focused on addressing these limitations and simplifying the computationally intensive problems. A novel adaptable and flexible simulation-based meta-heuristic approach is employed to determine suitable locations for public HCFs by integrating Geographic Information Systems (GIS) with Agent-Based Models (ABM). Intelligent, adaptable and autonomous spatial and non-spatial agents are utilized to interact with each other and the geographic environment, while taking independent decisions governed by spatial rules, such as •containment, •adjacency, •proximity and •connectivity. Three concepts are introduced: assess the coverage of existing HCFs using travel-time along the road network and determine the different average values of the service distance; endogenously determine the number and suitable locations of HCFs by integrating capacity and locational suitability constraints for maximizing coverage within the prevailing service distance; endogenously determine the distance constraint as the maximum distance between the population not covered within the desired service distance and its closest facility. The models’ validations on existing algorithms produce comparable and better results. With confirmed transferability, the thesis is applied to Lagos State, Nigeria in a disaggregated analysis that reflects spatial heterogeneity, to provide improved service coverage for healthcare. The assessment of the existing health service coverage and spatial distribution reveals disparate accessibility and insufficiency of the HCFs whose locations do not factor in the spatial distribution of the population. Through the application of the simulation-based approach, a cost-effective complete health service coverage is achieved with new HCFs. The spatial pattern and autocorrelation analysis reveal the influence of population distribution and geographic phenomenon on HCF location. The relationship of selected HCFs with other spatial features indicates agents’ compliant with spatial association. This approach proves to be a better alternative in resource constrained systems. The adaptability and flexibility meet the global health coverage agenda, the desires of the decision maker and the population, in the support for public health service coverage. In addition, a general theory of the system for a better-informed decision and analytical knowledge is obtained