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    • 9. 发明申请
    • Method of refining tall oil
    • 炼油方法
    • US20050182240A1
    • 2005-08-18
    • US11060938
    • 2005-02-18
    • Juhani Saviainen
    • Juhani Saviainen
    • B01D3/42C08G2/00C10G7/00C10G7/12C11B20060101C11B13/00
    • C10G7/12C10G7/00
    • The present invention relates to a method of refining crude tall oil. According to the method, the crude tall oil is fed into a column distiller, where it is separated into desired fractions. The conditions of the column distiller are adjusted on the basis of the composition of the feed and the product flow. According to the present invention, the densities of the crude tall oil fed into the column distiller and of at least one product flow are measured accurately as a function of the temperature. The density values are compared with correlation coefficients derived from the chemical compositions of the flows, coefficients which are obtained on the basis of laboratory analyses made earlier from corresponding flows, by using temperature compensation to specify the correlations, and by taking account of the regular variation in the wood composition of fresh wood according to the annual cycle and, as a result, the compositions of the flows are achieved. The present invention decreases the number of chemical analyses and improves the uniformity of the products.
    • 通过将粗妥质油加入柱蒸馏器中,将已知收获时间的松木制浆回收的粗妥油澄清,将其分离成所需馏分,其中至少一部分作为产物流回收。 用已知收获时间从松木制浆中回收的原油妥尔油炼制将粗的妥尔油进料到柱蒸馏器中,将其分离成所需的馏分,其中至少一部分作为产物流回收。 测量进料到柱蒸馏器中的原料妥尔油和产物流的密度。 密度测量精度为> = 4位小数,或相应地,5个有效数字作为温度的函数。 通过使用从流动的化学成分得到的相关函数,从密度值计算流动的组成。 相关函数基于对使用温度补偿的相应流量的实验室分析来指定相关性,并考虑到新鲜木材的木材组成中的常规年度周期变化,并且对柱蒸馏器的条件进行调整 饲料和产品流的组成的基础。 还包括用于测量液体流动密度的装置的独立权利要求,以测量原始妥尔油蒸馏酒的进料和产物流的密度,并可重复地确定妥尔油或其馏分的液体流量的密度, 精度> = 4位小数或相应的5个有效数字。
    • 10. 发明授权
    • Control system using an adaptive neural network for target and path
optimization for a multivariable, nonlinear process
    • 控制系统使用自适应神经网络进行多变量非线性过程的目标和路径优化
    • US5477444A
    • 1995-12-19
    • US944645
    • 1992-09-14
    • Naveen V. BhatWilliam B. BradenKent E. HeckendoornTimothy J. GraettingerAlexander J. FederowiczPaul A. DuBose
    • Naveen V. BhatWilliam B. BradenKent E. HeckendoornTimothy J. GraettingerAlexander J. FederowiczPaul A. DuBose
    • C10G7/00B01D3/42B01J19/00C10G7/12G05B11/32G05B13/02G05B13/04G05B19/02G06F15/18G06N3/00G06F15/46
    • G05B13/027B01D3/425B01J19/0033Y10S706/903Y10S706/906
    • A control system having four major components: a target optimizer, a path optimizer, a neural network adaptation controller and a neural network. In the target optimizer, the controlled variables are optimized to provide the most economically desirable outputs, subject to operating constraints. Various manipulated variable and disturbance values are provided for modeling purposes. The neural network receives as inputs a plurality of settings for each manipulated and disturbance variable. For target optimization all the neural network input values are set equal to produce a steady state controlled variable value. The entire process is repeated with differing manipulated variable values until an optimal solution develops. The resulting target controlled and manipulated variable values are provided to the path optimizer to allow the manipulated variables to be adjusted to obtain the target output. Various manipulated variable values are developed to model moves from current to desired values. In this case trend indicating values of the manipulated and disturbance variables are provided to produce time varying values of the controlled variables. The process is repeated until an optimal path is obtained, at which time the manipulated variable values are applied to the actual process. On a periodic basis all of the disturbance, manipulated and controlled variables are sampled to find areas where the training of the neural network is sparse or where high dynamic conditions are indicated. These values are added to the set of values used to train the neural network.
    • 具有四个主要部分的控制系统:目标优化器,路径优化器,神经网络适配控制器和神经网络。 在目标优化器中,受控变量进行了优化,以提供最经济可取的输出,受操作限制。 提供各种操纵变量和干扰值用于建模目的。 神经网络接收每个被操纵和干扰变量的多个设置作为输入。 对于目标优化,所有神经网络输入值都被设置为等于产生稳态受控变量值。 使用不同的操纵变量值重复整个过程,直到发展出最佳解。 所产生的目标控制和操纵的变量值被提供给路径优化器,以允许调整操纵的变量以获得目标输出。 开发了各种操纵变量值,以模拟从当前值到期望值的移动。 在这种情况下,提供了指示受控和干扰变量值的趋势,以产生受控变量的时变值。 重复该过程直到获得最佳路径,此时将操纵的变量值应用于实际过程。 周期性地对所有扰动,操纵和控制变量进行采样,以找到神经网络的训练稀疏或指示高动态条件的区域。 这些值被添加到用于训练神经网络的值集合中。