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Development of a novel coal preparation plant simulator using genetic algorithms based approach.

机译：使用基于遗传算法的方法开发新型选煤厂模拟器。

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摘要

Predictably, achievement of maximum profitability is a major goal for modern coal preparation plant operators. It can be realized through the maximization of the clean coal yield while satisfying product quality specifications and minimization of the preparation cost. One way to reduce the coal preparation cost and increase plant profitability is to increase the amount of clean coal (yield) produced from the same rate of feed coal without increasing the total cost. This can be accomplished by either replacing the existing processes with more cost-effective processes or using advanced control techniques to run the preparation plant at its highest performance by optimally setting the operating conditions. An established necessary condition for the maximization of plant overall clean coal yield is to equalize the incremental (or elemental) product quality, such as the incremental ash content of each cleaning circuit. Although this approach has been known to be quite successful while addressing single product quality, it is known to have limitations when dealing with multiple product quality constraints.;In this study, a novel coal preparation plant simulator was developed that not only maximizes the overall clean coal yield of various plant flowsheet configurations, but also identifies the most profitable plant flowsheet. The simulator identifies the appropriate operating conditions of the best flowsheet that would maximize the plant yield. The developed simulator uses a genetic algorithms (GA) based optimization technique to maximize the overall clean coal yield for each flowsheet alternative while satisfying multiple product quality constraints. The net present value (NPV) analysis is utilized to maximize the plant profitability over the total life span of a new preparation plant or the remaining life of an existing plant.;Empirical models for predicting the performance of a majority of units were developed using actual experimental data collected from multiple plant testing exercises. A unified model was developed to suitably predict the separation performance from all density based processes. Empirical models were also developed for determining capital and operating costs for individual components of plant flowsheets primarily based on mine and mill equipment cost data from an established data bank of Western Mine Engineering, Inc.;This newly developed simulator was tested using size-by-size feed washability data from multiple mines. The results indicated that for a given feed coal material and target product quality specifications, the heavy medium based flowsheet usually generates more clean coal product (yield), but also requires higher capital investment and incurs a higher annual operating cost than a water based flowsheet. Therefore, economic analysis, such as NPV analysis, should be conducted instead of merely calculating the cost per ton of coal cleaning to determine the most beneficial plant flowsheet alternative.
机译：可以预见，实现最大利润是现代选煤厂经营者的主要目标。这可以通过在满足产品质量规格的同时最大化清洁煤产量并最小化制备成本来实现。降低选煤成本并提高工厂盈利能力的一种方法是增加以相同进料煤比例生产的洁净煤（产量）的数量，而不增加总成本。这可以通过用更具成本效益的过程代替现有过程，或通过优化设置操作条件，使用先进的控制技术以最高的性能运行制备工厂来实现。为了使工厂的整体洁净煤总产量最大化，已建立的必要条件是使增量（或元素）产品质量（例如每个清洁回路的增量灰分）均等。尽管已知这种方法在解决单一产品质量方面非常成功，但在处理多个产品质量约束时已知有局限性。在这项研究中，开发了一种新型的选煤厂模拟器，该模拟器不仅可以最大程度地提高整体清洁度各种工厂流程图配置的煤炭产量，还可以确定最有利可图的工厂流程图。该模拟器可以确定最佳流程的适当操作条件，从而最大程度地提高工厂的产量。开发的模拟器使用基于遗传算法（GA）的优化技术，以在满足多个产品质量约束的同时，最大化每个流程替代品的整体清洁煤产量。净现值（NPV）分析用于在新的准备工厂的整个使用寿命或现有工厂的剩余寿命中最大化工厂的获利能力;;使用实际模型开发了预测大多数机组性能的经验模型从多个工厂测试练习中收集的实验数据。开发了一个统一的模型以适当地预测所有基于密度的工艺的分离性能。还基于主要来自Western Mine Engineering，Inc.建立的数据库的矿山和工厂设备成本数据，开发了用于确定工厂工艺流程的各个组成部分的资本和运营成本的经验模型；该新开发的模拟器使用逐个尺寸进行了测试。多个矿山的大小饲料可洗性数据。结果表明，对于给定的给煤原料和目标产品质量规格，基于重介质的流程图通常会产生更多的洁净煤产品（产量），但与基于水的流程图相比，它还需要更高的资本投入和更高的年度运营成本。因此，应该进行经济分析（例如NPV分析），而不是仅仅计算每吨洗煤成本来确定最有利的工厂流程替代方案。

著录项

  • 作者

    Huang, Zhanyou.;

  • 作者单位

    Southern Illinois University at Carbondale.;

  • 授予单位 Southern Illinois University at Carbondale.;
  • 学科 Engineering Mining.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 184 p.
  • 总页数 184
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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