Advancing simulation-optimization modeling (SOM) techniques for the development of robust air and water pollution load reduction-allocation (PLRA) programs

推进模拟优化建模 (SOM) 技术，以开发稳健的空气和水污染负荷减少分配 (PLRA) 计划

• 批准号: 251325-2011
• 负责人: Liu, Lei
• 金额: $ 1.38万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570956
• 关键词: Advancing; simulation; optimization; modeling; SOM

Deteriorations of regional ambient air quality and natural surface water bodies are among the major environmental concerns facing decision makers and stakeholders. Effective air and water pollution load reduction-allocation (PLRA) programs are desired for air/water quality restoration and management. Simulation-optimization modeling (SOM) techniques play a central role in developing such PLRA programs, where the simulation model serves as a numerical representation of the prototype nonlinear air/water system to predict the effects of external forces (i.e. pollutant loads) on the internal responses (i.e. air/water quality); and optimization models are coupled to iteratively drive the simulation model to find the optimal PLRA strategies. The primary objective of this proposed research aims to develop advanced SOM techniques for a PLRA simulation-optimization analysis; it entails: (a) characterize the nonlinearities and uncertainties of complex air and water systems, (b) propose nonlinearity-to-uncertainty mapping (N2UM) approaches to convert the original nonlinear SOM model to its uncertain LP equivalence and investigate its performance over existing functional approaximators, (c) develop advanced inexact optimization algorithms for solving LP-equivalent models under multiple uncertainties, and (d) develop interactive post-optimality approaches to reflect the tradeoffs between decision risk and system safety for generating operational decision schemes. The developed SOM techniques will be applied to real-world cases within a Canadian air and water context. The proposed approaches represent a unique contribution to methodologies in the PLRA field. It could provide technical supports for tackling challenges in terms of computation bottleneck, uncertainty handling, and decision-risk & system-safety tradeoff analysis. Its applications to Canadian cases will help Canadian stakeholder improve their effectiveness and efficiency in managing air/water pollution problems. Besides, a number of graduate students and PDFs will be trained through participating in the proposed research. The trained HQPs will fill a pressing need for environmental management professionals in various sectors.

区域环境空气质量和天然地表水体的恶化是决策者和利益相关者面临的主要环境问题。需要有效的空气和水污染减少分配（PLRA）计划，以用于空气/水质恢复和管理。模拟优化建模（SOM）技术在开发此类PLRA程序中起着核心作用，在此过程中，模拟模型是原型非线性空气/水系统的数值表示，以预测外力（即污染物负载）对内部响应的影响（即空气/水质质量）；优化模型与迭代驱动模拟模型相连，以找到最佳的PLRA策略。这项拟议研究的主要目标旨在开发用于PLRA模拟优化分析的先进SOM技术。它需要：（a）表征复杂的空气和水系统的非线性和不确定性，（b）提出非线性到不确定性映射（N2UM）方法，以将原始的非线性SOM模型转换为其不确定的LP等效性，并研究其对现有功能批准剂的不确定性效果，（C）开发了不符合的不符合的范围，（C）开发了不符合的范围，（c）Equive soltective and Equive soltective（C）互动后的术后方法反映了制定运营决策方案的决策风险与系统安全之间的权衡。开发的SOM技术将应用于加拿大空气和水环境中的现实情况。所提出的方法代表了对PLRA领域方法论的独特贡献。它可以提供技术支持，以解决计算瓶颈，不确定性处理以及决策风险和系统安全权衡分析方面的挑战。它在加拿大案件中的应用将有助于加拿大利益相关者提高其在管理空气/水污染问题方面的有效性和效率。此外，许多研究生和PDF将通过参加拟议的研究而接受培训。受过训练的HQP将满足各个部门环境管理专业人员的紧迫需求。