CN108182337A - 一种基于ScMiUKFNN算法的天然气净化工艺建模方法 - Google Patents

一种基于ScMiUKFNN算法的天然气净化工艺建模方法 Download PDF

Info

Publication number
CN108182337A
CN108182337A CN201810200655.1A CN201810200655A CN108182337A CN 108182337 A CN108182337 A CN 108182337A CN 201810200655 A CN201810200655 A CN 201810200655A CN 108182337 A CN108182337 A CN 108182337A
Authority
CN
China
Prior art keywords
neural network
network model
state variable
sample
state
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201810200655.1A
Other languages
English (en)
Other versions
CN108182337B (zh
Inventor
辜小花
王甜
唐海红
张堃
宋鸿飞
张兴
侯松
裴仰军
李太福
邱奎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University of Science and Technology
Original Assignee
Chongqing University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University of Science and Technology filed Critical Chongqing University of Science and Technology
Priority to CN201810200655.1A priority Critical patent/CN108182337B/zh
Publication of CN108182337A publication Critical patent/CN108182337A/zh
Application granted granted Critical
Publication of CN108182337B publication Critical patent/CN108182337B/zh
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Gas Separation By Absorption (AREA)
  • Treating Waste Gases (AREA)

Abstract

本发明公开了一种基于ScMiUKFNN算法的天然气净化工艺建模方法,包括以下步骤: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;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;步骤S3:形成归一化样本集,取所述归一化样本集中一部分作为训练样本,剩余部分作为测试样本;步骤S4:基于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;步骤S5:利用ScMiUKFNN算法估计所述神经网络模型的最优状态变量;步骤S6:获得训练样本更新后的神经网络模型;步骤S7:得到预测结果,将预测结果与所述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,神经网络模型有效;否则重复上述步骤至比较结果小于预设误差值。

Description

一种基于ScMiUKFNN算法的天然气净化工艺建模方法
技术领域
本发明涉及高含硫天然气净化技术领域,更为具体地,涉及一种基于ScMiUKFNN算法的天然气净化工艺建模方法。
背景技术
随着清洁能源需求的快速增长,天然气的需求在逐渐增加。然而,高含硫气体(high-sulfur gas,HSG)含酸性气体比一般天然气多十倍,在中国的气藏中占有相当大的比例。由于具有毒性和腐蚀性,高含硫气体不能直接使用,必须去除硫化氢(H2S)和二氧化碳(CO2),称为高含硫气体脱硫过程。此外,随着酸性气体吸收剂溶液循环的增加,这一过程的能量消耗和生产成本急剧增加。因此,降低能源消耗和运行成本,提高企业的经济效益,提高市场竞争力,是一个急待解决的问题。
发明内容
本发明的目的在于克服现有技术的不足,提供一种基于ScMiUKFNN算法的天然气净化工艺建模方法
本发明的目的是这样实现的:
一种基于ScMiUKFNN算法的天然气净化工艺建模方法,本方法包括以下步骤:
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;
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一部分样本作为训练样本,剩余部分的样本作为测试样本;
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θk,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;
所述神经网络模型为:
其中,为所述训练样本的矢量样本值,并作为所述神经网络模型的输入;zj作为所述神经网络wij模型的隐含层输出;作为所述神经网络模型的输出层输出;wij为神经网络模型的输入层到隐含层的神经元的连接权值;为神经网络模型的输入层到隐含层的神经元的阈值;vjd为所述神经网络模型的隐含层到输出层的神经元的连接权值,为所述神经网络模型的隐含层到输出层的神经元的阈值,i=1,2,…,m;m为神经网络模型的输入层的神经元的数量,s为神经网络模型的隐含层的神经元的数量,h为神经网络模型的输出层的神经元的数量;
应用于神经网络模型各层神经元的非线性激活函数为:
fo(x)=x (4)
所述初始状态变量为:
步骤S5:利用ScMiUKFNN算法估计所述神经网络模型的最优状态变量;
步骤S6:将所述最优状态变量作为所述神经网络模型的wij、vjd对公式(1)和公式(2)进行更新,获得训练样本更新后的神经网络模型;
步骤S7:将所述测试样本中的输入到更新后的神经网络模型,得到预测结果,将所述预测结果与所述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;否则重复上述步骤S1-S7,直至所述比较结果小于所述预设误差值为止。
优选地,所述步骤S5包括:
步骤S51:在建立的所述神经网络模型中,将参数向量视为算法所需的状态方程,网络输出视为量测方程:
其中,为神经网络模型的输入,为神经网络模型的输出,是参数化的非线性函数,ηk是过程噪声,μk是测量噪声;
对状态方程和量测方程进行初始化,计算状态变量估计以及其协方差:
其中:是状态值,Pk是协方差矩阵;
步骤S52:引入一个最小的sigma集合,运用减少Sigma点集方法对所述初始状态变量θk进行Sigma采样,获得n+1个采样点以及权重系数,以减少计算复杂度;随机变量具有均值和协方差矩阵PXX>0,则:
Wweight=[W ωn+1] (9)
其中:
“:=”为赋值号;
步骤S53:状态更新,通过离散时间非线性系统的状态方程将每个采样点的k时刻的最优状态变量的状态估计变换为k+1时刻的状态变量的状态估计并通过合并k+1时刻的状态估计的向量,获得k+1时刻的状态变量的状态先验估计和协方差其中,所述状态估计为:
其中,β为放缩因子,f为线性方程;
所述状态先验估计为:
所述状态变量的协方差为:
步骤S54:量测更新,通过离散时间非线性系统的量测方程建立k时刻的状态变量的状态估计和k时刻的量测预测估计之间的联系以完成量测预测,并估计k时刻的量测预测的协方差以及k时刻的状态变量和量测预测之间的协方差其中,所述量测估计为:
所述k时刻的量测预测的均值为:
所述k时刻的量测预测的协方差为:
所述k时刻的状态变量和量测预测之间的协方差为:
步骤S55:通过建立协方差和协方差的关系,更新k时刻的状态变量的状态估计和协方差;
所述协方差之间的关系是:
通过上述关系对k+1时刻的状态变量的状态估计和协方差进行修正:
步骤S56:将获得的修正后k+1时刻的状态变量重组神经网络模型,并计算此时神经网络模型的预测输出与实际输出之间的误差,如果小于既设精度要求,则输出所述神经网络模型的最优状态变量反之,重新进入步骤1。
优选地,所述工艺参数包括进入尾气吸收塔的贫胺液流量、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;脱硫单元的性能指标包括净化气中H2S和CO2的浓度。
优选地,步骤S3中,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。
由于采用了上述技术方案,本发明相对于现有技术能够节能降耗,提高产率和气体加工经济效益。
附图说明
图1a、图1b为比较预测结果和运行数据图;
图2a、图2b为对模型精度的比较图;
图3a、图3b为比较预测结果和运行数据图。
具体实施方式
名词解释
ScMiUKFNN:Scaled Minimum Unscented Kalman Filter Neural Network,基于比例放缩的减少采样无迹卡尔曼滤波神经网络。
本发明提供的基于ScMiUKFNN算法的工业过程建模方法,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,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;脱硫单元的性能指标包括净化气中H2S和CO2的浓度。参数列表如表1所示:
表1
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];样本集[X,Y]如下表2所示:
表2
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θk,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:
其中,为所述训练样本的矢量样本值,并作为所述神经网络模型的输入;zj作为所述神经网络隐含层输出;作为所述神经网络输出层输出;wij为网络输入层到隐含层的神经元的连接权值;为网络输入层到所述隐含层的神经元的阈值;vjd为所述隐含层到网络输出层的神经元的连接权值,为所述隐含层到所述网络输出层的神经元的阈值,其中,i=1,2,…,m;m为所述网络输入层的神经元的数量,s为所述网络隐含层的神经元的数量,h为所述网络输出层的神经元的数量;
其中,应用于各层神经元的非线性激活函数为:
fo(x)=x (4)
所述初始状态变量为:
步骤S5:利用ScMiUKFNN算法估计所述神经网络模型的最优状态变量;
本发明利用ScMiUKFNN算法估计神经网络模型的状态变量,以达到连接权值、阈值的不断调整,直到满足要求。将得到的最优状态变量的状态估计作为上述所建立神经网络模型的连接权值、阈值。需要说明的是,该连接权值、阈值为通过ScMiUKFNN算法调整后的连接权值、阈值,也是上述所建立的神经网络模型的全部连接权值与阈值,包括wij、vjd
利用ScMiUKFNN算法估计神经网络模型的最优状态变量的过程包括:
步骤S51:在建立的所述神经网络模型中,将参数向量视为算法所需的状态方程,网络输出可视为量测方程:
其中,为神经网络模型的输入,为神经网络模型的输出,是参数化的非线性函数,ηk是过程噪声,μk是测量噪声。
并对两个方程进行初始化,计算状态变量估计以及其协方差:
其中:是状态值,Pk是协方差矩阵;
步骤S52:引入了一个最小的sigma集合,运用减少Sigma点集方法对所述初始状态变量θk进行Sigma采样,获得n+1个采样点以及权重系数,以减少计算复杂度;随机变量具有均值和协方差矩阵PXX>0,则:
Wweight=[W ωn+1] (9)
其中:
步骤S53:状态更新,通过离散时间非线性系统的状态方程将每个采样点的k时刻的最优状态变量的状态估计变换为k+1时刻的状态变量的状态估计并通过合并k+1时刻的状态估计的向量,获得k+1时刻的状态变量的状态先验估计和协方差其中,所述状态估计为:
其中,β为放缩因子;
所述状态先验估计为:
所述状态变量的协方差为:
步骤S54:量测更新,通过离散时间非线性系统的量测方程建立k时刻的状态变量的状态估计和k时刻的量测预测估计之间的联系以完成量测预测,并估计k时刻的量测预测的协方差以及k时刻的状态变量和量测预测之间的协方差其中,所述量测估计为:
所述k时刻的量测预测的均值为:
所述k时刻的量测预测的协方差为:
所述k时刻的状态变量和量测预测之间的协方差为:
步骤S55:通过建立协方差和协方差的关系,更新k时刻的状态变量的状态估计和协方差;
所述协方差之间的关系是:
通过上述关系对k+1时刻的状态变量的状态估计和协方差进行修正:
步骤S56:将获得的修正后k+1时刻的状态变量重组BP神经网络模型,并计算此时的模型预测输出与实际输出之间的误差,如果小于既设精度要求,则输出所述神经网络模型的最优状态变量反之,重新进入步骤1
步骤S6:将所述最优状态变量作为所述神经网络模型的wij、vjd对式(1)与(2)进行更新,获得所述训练样本更新后的神经网络模型;
步骤S7:将所述测试样本中的输入到更新后的神经网络模型,得到预测结果,将所述预测结果与所述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;否则重复上述步骤S1-S7,直至所述比较结果小于所述预设误差值为止。
本发明通过几组测试得到如下的技术效果:
图1a-图1b为比较预测结果和运行数据图,其中,图1a在训练阶段的H2S浓度,显示H2S浓度下降的散点图和列车数据集的三种模型估计,图1b在训练阶段的CO2浓度,在预测二氧化碳浓度方面对三种模型的性能进行了比较。
图2a-图2b为对模型精度的比较图,其中,图2a在训练阶段的H2S浓度,图2b训练阶段的CO2浓度。
图3a-图3b为比较预测结果和运行数据图,其中,图3a在训练阶段的H2S浓度,图3b训练阶段的CO2浓度。
对于超过80%的数据点,ScMiUKFNN模型估计的H2S浓度和CO2浓度的绝对相对误差小于10%,验证了所提出的模型的准确性,故所建模有效。
以上所述,仅为本发明的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到变化或替换,都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应所述以权利要求的保护范围为准。

Claims (4)

1.一种基于ScMiUKFNN算法的天然气净化工艺建模方法,其特征在于,本方法包括以下步骤:
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;
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一部分样本作为训练样本,剩余部分的样本作为测试样本;
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θk,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;
所述神经网络模型为:
其中,为所述训练样本的矢量样本值,并作为所述神经网络模型的输入;zj作为所述神经网络模型的隐含层输出;作为所述神经网络模型的输出层输出;wij为神经网络模型的输入层到隐含层的神经元的连接权值;为神经网络模型的输入层到隐含层的神经元的阈值;vjd为所述神经网络模型的隐含层到输出层的神经元的连接权值,为所述神经网络模型的隐含层到输出层的神经元的阈值,i=1,2,…,m;m为神经网络模型的输入层的神经元的数量,s为神经网络模型的隐含层的神经元的数量,h为神经网络模型的输出层的神经元的数量;
应用于神经网络模型各层神经元的非线性激活函数为:
fo(x)=x (4)
所述初始状态变量为:
步骤S5:利用ScMiUKFNN算法估计所述神经网络模型的最优状态变量;
步骤S6:将所述最优状态变量作为所述神经网络模型的wij、vjd对公式(1)和公式(2)进行更新,获得训练样本更新后的神经网络模型;
步骤S7:将所述测试样本中的输入到更新后的神经网络模型,得到预测结果,将所述预测结果与所述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;否则重复上述步骤S1-S7,直至所述比较结果小于所述预设误差值为止。
2.根据权利要求1所述的一种基于ScMiUKFNN算法的天然气净化工艺建模方法,其特征在于,所述步骤S5包括:
步骤S51:在建立的所述神经网络模型中,将神经网络模型的权值和阈值组成的状态向量视为算法所需的状态方程,神经网络的模型视为量测方程:
其中,为神经网络模型的输入,为神经网络模型的输出,是参数化的非线性函数,ηk是过程噪声,μk是测量噪声;
对状态方程和量测方程进行初始化,计算状态变量估计以及其协方差:
其中:是状态值,Pk是协方差矩阵;
步骤S52:引入一个最小的sigma集合,运用减少Sigma点集方法对所述初始状态变量θk进行Sigma采样,获得n+1个采样点以及权重系数,以减少计算复杂度;随机变量具有均值和协方差矩阵PXX>0,则:
Wweight=[W ωn+1] (9)
其中:
步骤S53:状态更新,通过离散时间非线性系统的状态方程将每个采样点的k时刻的最优状态变量的状态估计变换为k+1时刻的状态变量的状态估计并通过合并k+1时刻的状态估计的向量,获得k+1时刻的状态变量的状态先验估计和协方差其中,所述状态估计为:
其中,β为放缩因子,f为线性方程;
所述状态先验估计为:
所述状态变量的协方差为:
步骤S54:量测更新,通过离散时间非线性系统的量测方程建立k时刻的状态变量的状态估计和k时刻的量测预测估计之间的联系以完成量测预测,并估计k时刻的量测预测的协方差以及k时刻的状态变量和量测预测之间的协方差其中,所述量测估计为:
所述k时刻的量测预测的均值为:
所述k时刻的量测预测的协方差为:
所述k时刻的状态变量和量测预测之间的协方差为:
步骤S55:通过建立协方差和协方差的关系,更新k时刻的状态变量的状态估计和协方差;
所述协方差之间的关系是:
通过上述关系对k+1时刻的状态变量的状态估计和协方差进行修正:
步骤S56:将获得的修正后k+1时刻的状态变量重组神经网络模型,并计算此时神经网络模型的预测输出与实际输出之间的误差,如果小于既设精度要求,则输出所述神经网络模型的最优状态变量反之,重新进入步骤1。
3.根据权利要求1所述的一种基于ScMiUKFNN算法的天然气净化工艺建模方法,其特征在于,所述工艺参数包括进入尾气吸收塔的贫胺液流量、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;脱硫单元的性能指标包括净化气中H2S和CO2的浓度。
4.根据权利要求1所述的一种基于ScMiUKFNN算法的天然气净化工艺建模方法,其特征在于,步骤S3中,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。
CN201810200655.1A 2018-03-12 2018-03-12 一种基于ScMiUKFNN算法的天然气净化工艺建模方法 Active CN108182337B (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810200655.1A CN108182337B (zh) 2018-03-12 2018-03-12 一种基于ScMiUKFNN算法的天然气净化工艺建模方法

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810200655.1A CN108182337B (zh) 2018-03-12 2018-03-12 一种基于ScMiUKFNN算法的天然气净化工艺建模方法

Publications (2)

Publication Number Publication Date
CN108182337A true CN108182337A (zh) 2018-06-19
CN108182337B CN108182337B (zh) 2021-07-09

Family

ID=62553387

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810200655.1A Active CN108182337B (zh) 2018-03-12 2018-03-12 一种基于ScMiUKFNN算法的天然气净化工艺建模方法

Country Status (1)

Country Link
CN (1) CN108182337B (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116127729A (zh) * 2022-12-28 2023-05-16 青芥一合碳汇(武汉)科技有限公司 基于线性动态模型的二氧化碳捕集精准预测方法及系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050210103A1 (en) * 2001-12-03 2005-09-22 Microsoft Corporation Automatic detection and tracking of multiple individuals using multiple cues
CN103345559A (zh) * 2013-07-10 2013-10-09 重庆科技学院 铝电解过程电解槽工艺能耗的动态演化建模方法
CN106777866A (zh) * 2016-11-14 2017-05-31 重庆科技学院 面向节能降耗的高含硫天然气净化工艺建模与优化方法
CN106777468A (zh) * 2016-11-14 2017-05-31 重庆科技学院 高含硫天然气脱硫工艺强跟踪演化建模方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050210103A1 (en) * 2001-12-03 2005-09-22 Microsoft Corporation Automatic detection and tracking of multiple individuals using multiple cues
CN103345559A (zh) * 2013-07-10 2013-10-09 重庆科技学院 铝电解过程电解槽工艺能耗的动态演化建模方法
CN106777866A (zh) * 2016-11-14 2017-05-31 重庆科技学院 面向节能降耗的高含硫天然气净化工艺建模与优化方法
CN106777468A (zh) * 2016-11-14 2017-05-31 重庆科技学院 高含硫天然气脱硫工艺强跟踪演化建模方法

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
HENRIQUE M. MENEGAZ,ET AL: "A new smallest sigma set for the Unscented Transform and its applications on SLAM", 《IEEE》 *
MENEGAZ H M,ET AL: "Scaled Minimum Unscented Multiple Hypotheses Mixing Filter", 《IEEE》 *
杜清潭: "基于新型卡尔曼滤波的异步电机无传感器控制系统研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116127729A (zh) * 2022-12-28 2023-05-16 青芥一合碳汇(武汉)科技有限公司 基于线性动态模型的二氧化碳捕集精准预测方法及系统
CN116127729B (zh) * 2022-12-28 2023-08-15 青芥一合碳汇(武汉)科技有限公司 基于线性动态模型的二氧化碳捕集精准预测方法及系统

Also Published As

Publication number Publication date
CN108182337B (zh) 2021-07-09

Similar Documents

Publication Publication Date Title
CN106777866B (zh) 面向节能降耗的高含硫天然气净化工艺建模与优化方法
CN103927412B (zh) 基于高斯混合模型的即时学习脱丁烷塔软测量建模方法
CN106777465A (zh) 高含硫天然气净化工艺动态演化建模与节能优化方法
CN104636600B (zh) 基于极限学习机的高含硫天然气净化工艺建模、优化方法
CN108647373A (zh) 一种基于xgboost模型的工业过程软测量方法
CN108509692A (zh) 一种基于MiUKFNN算法的高含硫天然气脱硫工艺建模方法
Quilty et al. A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting–A case study in the Awash River Basin (Ethiopia)
CN106777466B (zh) 基于st-upfnn算法的高含硫天然气净化工艺的动态演化建模方法
CN112288193A (zh) 基于注意力机制的gru深度学习的海洋站表层盐度预测方法
Zhang et al. A novel variable selection algorithm for multi-layer perceptron with elastic net
CN113325702B (zh) 一种曝气控制方法及装置
CN108182337A (zh) 一种基于ScMiUKFNN算法的天然气净化工艺建模方法
Dashti et al. Estimation of CO2 equilibrium absorption in aqueous solutions of commonly used amines using different computational schemes
Li et al. Development of a novel soft sensor with long short-term memory network and normalized mutual information feature selection
CN106777468A (zh) 高含硫天然气脱硫工艺强跟踪演化建模方法
Olaleye et al. Performance assessment of control loops with time-variant disturbance dynamics
CN110442991A (zh) 一种基于参数化fir模型的动态硫回收软测量建模方法
Shi et al. Time-delay neural network for the prediction of carbonation tower's temperature
Asil et al. Reliable estimation of optimal sulfinol concentration in gas treatment unit via novel stabilized MLP and regularization network
CN116679026A (zh) 自适应无偏有限脉冲响应滤波的污水溶解氧浓度估计方法
CN113393905B (zh) 一种化学吸收co2捕集系统的动态鲁棒软测量系统及方法
CN115829157A (zh) 基于变分模态分解和Autoformer模型的化工水质指标预测方法
CN115936061A (zh) 基于数据驱动的火电厂烟气含氧量软测量方法及系统
CN112598178A (zh) 一种基于贝叶斯框架下的大气污染物浓度预测方法
Liu et al. A NOx emission forecasting model using multiresolution LSSVM based on data-driven and SF-KPCA

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Guangzhou Senyu automation machinery design Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980040566

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230830

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Foshan shangxiaoyun Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041008

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230906

Application publication date: 20180619

Assignee: FOSHAN YAOYE TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041004

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230906

Application publication date: 20180619

Assignee: FOSHAN YIQING TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041003

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230906

Application publication date: 20180619

Assignee: Guangzhou Fuke Machinery Trade Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980040999

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230906

Application publication date: 20180619

Assignee: Guangzhou trump Environmental Protection Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980040995

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230906

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Wokang (Guangzhou) Electronic Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041471

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230913

Application publication date: 20180619

Assignee: Changhong (Guangzhou) Information Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041467

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230913

Application publication date: 20180619

Assignee: Dongguan Yaluo Environmental Protection Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041460

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230913

EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Laishi (Guangzhou) Digital Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041991

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230922

Application publication date: 20180619

Assignee: Guangzhou Qiming Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041990

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230922

Application publication date: 20180619

Assignee: Guangzhou Daguan Digital Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041989

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230922

Application publication date: 20180619

Assignee: Yichang Dae Urban and Rural Construction Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041988

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230922

Application publication date: 20180619

Assignee: Guangzhou Dongtong Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041866

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230922

Application publication date: 20180619

Assignee: Dongguan Zhaoyi Information Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041863

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230922

Application publication date: 20180619

Assignee: Leta (Guangzhou) Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980041859

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20230922

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: GUANGZHOU KUAIZHOU INTELLIGENT ENVIRONMENTAL TECHNOLOGY CO.,LTD.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044603

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: Guangzhou Tuyu Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044600

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: GUANGZHOU SHANGCHENG TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044597

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: GUANGZHOU JUFENG TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044596

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: GUANGZHOU XINGYIN TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044593

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: GUANGZHOU LVNENG INTELLIGENT TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044591

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: Guangzhou Xiaoqing Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044587

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: Guangzhou Fangshao Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044586

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

Application publication date: 20180619

Assignee: Guangzhou star automation equipment Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980044559

Denomination of invention: A Modeling Method for Natural Gas Purification Process Based on ScMiUKFNN Algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231031

EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Guangzhou Yuming Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047712

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

Application publication date: 20180619

Assignee: Yajia (Guangzhou) Electronic Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047706

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

Application publication date: 20180619

Assignee: Guangzhou Yibo Yuntian Information Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047705

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

Application publication date: 20180619

Assignee: GUANGZHOU XIAONAN TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047703

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

Application publication date: 20180619

Assignee: GUANGZHOU YIDE INTELLIGENT TECHNOLOGY Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047702

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

Application publication date: 20180619

Assignee: Lingteng (Guangzhou) Electronic Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047701

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

Application publication date: 20180619

Assignee: Guangzhou Taipu Intelligent Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047700

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

Application publication date: 20180619

Assignee: Yuxin (Guangzhou) Electronic Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980047695

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231124

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Guangxi GaoMin Technology Development Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2023980053986

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20231227

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Yuao Holdings Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2024980000640

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20240119

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Foshan chopsticks Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2024980003017

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20240322

Application publication date: 20180619

Assignee: Foshan qianshun Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2024980003012

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20240322

EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180619

Assignee: Foshan helixing Technology Co.,Ltd.

Assignor: Chongqing University of Science & Technology

Contract record no.: X2024980004524

Denomination of invention: A modeling method for natural gas purification process based on ScMiUKFNN algorithm

Granted publication date: 20210709

License type: Common License

Record date: 20240419

EE01 Entry into force of recordation of patent licensing contract