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Simulation optimization based process control for groundwater remediation under uncertainty.

机译：基于仿真优化的不确定性地下水修复过程控制。

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摘要

In this dissertation research, recently developed Artificial Intelligence (AI) integrated site management and process control systems for petroleum contaminated sites have been reported.;To increase the accuracy of the simulation of the contaminant fates and transport in groundwater, Optimized Linear Interpolation Method (OLIM) has been developed as an enhancement of the 3D simulation model---UTCHEM [oTaA00a], which enables UTCHEM to handle the uncertainty that arises due to simplification and assumption of input site data.;For the process control of in-situ bioremediation process, an artificial intelligence aided process control system (FESCS) was first developed for a petroleum contaminated site (The Cantuar Site) which is located in Saskatchewan, 250 km west of Regina. A hybrid fuzzy predictive process control system (FMPCS) was then developed, which was based on the coupling of the numerical simulation model with fuzzy rule-based expert system.;A dynamic, online predictive control system (MPCISB) was then developed. MPCISB consists of a numerical simulation model and an optimization function. It can be applied for the site where no sufficient control knowledges available. The development of a dynamic knowledge-based reasoning enhanced model predictive control system (KBRECS) was then presented. KBRECS includes an optimization subsystem and a monitoring subsystem. The strength of KBRECS is its ability to identify new control strategy promptly without loss in control quality when the site status is different from the predicted value. Last, an interactive multi-objective decision-making tool (MODPCISB) was presented. MODPCISB shares the objective of minimizing overall cost as that considered in previous developed control systems; it also has the added objective of maximizing system efficiency.;For the site management purpose, a fuzzy expert system based decision support system (RSES) was constructed for the remediation technology selection of contaminated site. RSES includes two sub-systems---Site Characterization Sub-System (SCSS) and Site Visualization Sub-System (SVSS). SCSS is a rule-based fuzzy expert system, with the capability for dealing with imprecision in the inputs on site conditions. SVSS can visualize the multi-dimensional soil type and contaminant concentration distribution data on two-dimensional map.;All control systems have been tested either by laboratory tank data, real world site data or by hypothetical site data. The results from the case studies indicate that those control systems can achieve their desired objective.
机译：在本论文的研究中，已报道了最近开发的用于石油污染场所的人工智能（AI）集成场所管理和过程控制系统。为了提高模拟污染物在地下水中的含量和运移的准确性，优化了线性插值法（OLIM） ）已开发为3D仿真模型-UTCHEM [oTaA00a]的增强，它使UTCHEM能够处理由于简化和假定输入位点数据而引起的不确定性。；用于原位生物修复过程的过程控制，首先为位于里贾纳以西250公里处萨斯喀彻温省的一个石油污染现场（坎图尔现场）开发了一个人工智能辅助过程控制系统（FESCS）。然后，基于数值模拟模型与基于模糊规则的专家系统的耦合，开发了一种混合式模糊预测过程控制系统（FMPCS）。然后，开发了动态在线预测控制系统（MPCISB）。 MPCISB由一个数值模拟模型和一个优化函数组成。它可以用于没有足够控制知识的站点。然后介绍了基于动态知识的推理增强模型预测控制系统（KBRECS）的开发。 KBRECS包括优化子系统和监视子系统。当站点状态与预测值不同时，KBRECS的优势在于它能够迅速识别新的控制策略而不会损失控制质量。最后，提出了一种交互式多目标决策工具（MODPCISB）。 MODPCISB的目标是将总体成本降至最低，这与先前开发的控制系统中所考虑的一样；出于场地管理的目的，构建了基于模糊专家系统的决策支持系统（RSES），用于污染场地的修复技术选择。 RSES包括两个子系统-站点特征子系统（SCSS）和站点可视化子系统（SVSS）。 SCSS是基于规则的模糊专家系统，具有处理现场条件输入中的不精确性的功能。 SVSS可以在二维地图上可视化多维土壤类型和污染物浓度分布数据。所有控制系统均已通过实验室水箱数据，实际站点数据或假设站点数据进行了测试。案例研究的结果表明，这些控制系统可以实现其期望的目标。

著录项

  • 作者

    Hu, Zhiying.;

  • 作者单位

    The University of Regina (Canada).;

  • 授予单位 The University of Regina (Canada).;
  • 学科 Engineering System Science.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 299 p.
  • 总页数 299
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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