专利详细信息

高含硫天然气脱硫工艺强跟踪演化建模方法

文献类型：专利

专利类型：发明专利

是否失效：否

是否授权：否

申请号：CN201611003680.8

申请日：20161114

发明人：辜小花杨利平李太福唐海红张利亚张堃邱奎

申请人：重庆科技学院

申请人地址：401331 重庆市沙坪坝区大学城东路20号

公开日：20170531

公开号：CN106777468A

代理人：王玉芝;杨明

代理机构：重庆蕴博君晟知识产权代理事务所(普通合伙)

语　　种：中文

摘　　要：本发明提供的高含硫天然气脱硫工艺强跟踪演化建模方法，包括：选取影响脱硫效率的工艺参数和脱硫单元的性能指标；采集预设时间的所述工艺参数和所述性能指标的数据，剔除误差样本后形成样本集；对样本集进行归一化形成归一化样本集，并从中选取训练样本和测试样本；基于训练样本构建神经网络模型并确定神经网络模型的初始状态变量；用ST‑UKFNN算法估计神经网络模型的最优状态变量；将最优状态变量作为神经网络模型的连接权值和阈值，即获得权值阈值更新后的神经网络模型；将测试样本输入到更新后的神经网络模型，得到预测结果，将预测结果与测试样本中的实际输出进行比较，如果比较结果小于预设误差值，所构建的神经网络模型有效。

主权项：1.一种高含硫天然气脱硫工艺强跟踪演化建模方法，包括：步骤S1：选择影响脱硫效率的工艺参数和脱硫单元的性能指标；其中，所述工艺参数包括进入尾气吸收塔贫的胺液流量、进入二级吸收塔的贫胺液流量、原料气处理量、尾气单元返回脱硫单元的半富胺液流量、一级吸收塔胺液入塔温度、二级吸收塔胺液入塔温度、闪蒸罐压力、一个重沸器的蒸汽消耗量、另一个重沸器的蒸汽消耗量和蒸汽预热器的蒸汽消耗量；所述脱硫单元的性能指标包括净化气中H₂S和CO₂的浓度以及净化气的产量；步骤S2：采集预设时间的所述工艺参数和所述性能指标的数据，剔除误差样本后形成样本集[X,Y]；步骤S3：对样本集[X,Y]进行归一化，形成归一化样本集取所述归一化样本集中前80％的样本作为训练样本，而剩余的20％样本作为测试样本；步骤S4：基于所述训练样本构建神经网络模型和所述神经网络模型的初始状态变量X，以及，将所述训练样本中的作为所述神经网络模型的输入，将所述训练样本中的作为所述神经网络模型的输出；其中，所述神经网络模型为： $<math> <mrow> <mi>Y</mi> <mo>=</mo> <munderover> <mo>Σ</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn</p><p class='subject'><strong>关键词：</strong>神经网络模型测试样本最优状态变量训练样本预测结果样本集构建预设初始状态变量 <b class='type_ico_id'></b>高含硫天然气归一化样本实际输出算法估计脱硫单元 <b class='type_ico_id'></b>脱硫工艺 <b class='type_ico_id'></b>脱硫效率误差样本演化建模阈值更新 <b class='type_ico_id'></b>归一化权值和剔除采集跟踪更新 </p><p class='class'><strong>IPC专利分类号：</strong>G06F17/50(20060101);G06N3/02(20060101)</p>
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高含硫天然气脱硫工艺强跟踪演化建模方法

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