Evaluation method and system for assessing the estimate of energy consumption per tonne in distillation processes

Abstract

The present disclosure discloses a method for evaluating estimation accuracy of energy consumption per ton in distillation processes, and belongs to the technical field of evaluation of estimation performance of energy consumption per ton in distillation processes. The method includes building a state space model of a distillation process, determining a state estimation model, and obtaining an estimated value of energy consumption per ton in the distillation process according to the state estimation model and the state space model; obtaining an estimated value of a state variable with the optimal overall evaluation using a determined evaluation function, describing interference information making the estimated value deviate from a true value and being reflected in an observed value, and transferring the interference information from the observed value to the estimated value of the state variable to obtain an estimation accuracy of the state variable; and unitizing the interference information affecting the estimated value, and evaluating the estimation accuracy of energy consumption per ton based on the unitized interference information. The present disclosure may well reflect the deviation between the estimated value and the true value without the true value, and evaluate the same object using different estimation methods under the same architecture, so that the evaluation results may cross different estimation methods and still have practicality.

Claims

1. A method and system for evaluating estimation accuracy of energy consumption per ton in distillation processes, wherein the method comprises: S1: building a state space model of a distillation process, obtaining a model predicted value based on the state space model, and obtaining an observed value of the distillation process, a method for building the state space model of the distillation process comprising: for a material balance problem, building a high-order model equation for the distillation process, a model with a five-dimensional structure being used, parameters of the distillation process model being changeable within a specific interval, and the concrete model form being:
x.sub.n=A.sub.nx.sub.n−1+E.sub.nu.sub.n+B.sub.nw.sub.n
y.sub.n=C.sub.nx.sub.n+v.sub.n wherein x.sub.n=[x.sub.n.sup.1, x.sub.n.sup.2, x.sub.n.sup.3, x.sub.n.sup.4, x.sub.n.sup.5].sup.T is a state vector of a higher-order model of the distillation process; n is the time; x.sub.n.sup.1 is the mole coefficient of low-density material components at the top in a distillation column; x.sub.n.sup.5 is the mole coefficient of low-density material components at the bottom in the distillation column; x.sub.n.sup.2, x.sub.n.sup.3, x.sub.n.sup.4 are state variables used in estimation of energy consumption per ton; u.sub.n is a controlled variable of a distillation column system; w.sub.n is input disturbance of the distillation column; v.sub.n is sensor disturbance in the distillation column; calculation forms of parameters A.sub.n, B.sub.n and C.sub.n of the distillation column model are: $A_n=\left[\begin{array}{ccccc}-2.9& 0.3& 0& 0& 0\\ 0.9& -1.2& 0.9& 0& 1.1\\ 2.4& 1.5& -4.9& 12.4& 3.2\\ 0& 0& 5.1& 11& 1.1\\ 0& 0& 0& 2.3& -3.9\end{array}\right]$ $B_n={\left[\begin{array}{ccccc}0& 0& 1.6& 0& 0\\ 0& -0.01& 0.02& 0.03& 0\end{array}\right]}^T$ $C_n=I_{1\times 5},I\mathrm{is}a\mathrm{unit}\mathrm{matrix};$ S2: determining a state estimation model based on the observed value and the model predicted value as follows:
{circumflex over (x)}.sub.n={circumflex over (x)}.sub.n.sup.−+K.sub.n(y.sub.n−C.sub.n{circumflex over (x)}.sub.n.sup.−) wherein n is the time, {circumflex over (x)}.sub.n.sup.− is the model predicted value, y.sub.n is the observed value, {dot over (x)}.sub.n is an estimated value of a state variable, and K.sub.n is an estimated gain; obtaining an estimated value of energy consumption per ton in the distillation process according to the state estimation model and the state space model, comprising: conducting state estimation at a certain time according to the state estimation model and the state space model to obtain a state estimated gain and an estimated value of the state variable at a current time; and obtaining an estimated value of energy consumption per ton based on the estimated value of the state variable, a calculation formula being: Ē.sub.n=f(x.sub.n)=1.25[S.sub.1x.sub.n.sup.1−S.sub.2x.sub.n.sup.5]+264.5; S3: obtaining an estimated value of the state variable with the optimal overall evaluation using a determined evaluation function, comprising: determining a mathematical expression of the evaluation function to be F({dot over (x)}.sub.n.sup.−, y.sub.n), and obtaining the estimated value ${\dot{x}}_n=\arg {\min }_{x_n}\frac{1}{T}\underset{i=1}{\overset{T}{.Math.}}F\left({\stackrel{.}{x}}_n^{-},y_n\right)$ of the state variable with the optimal overall evaluation by the evaluation function, wherein T is the duration from an initial time to the current time; describing interference information making the estimated value deviate from a true value and being reflected in the observed value, and transferring the interference information from the observed value to the estimated value of the state variable to obtain an estimation accuracy of the state variable, comprising: representing the interference information making the estimated value deviate from the true value and being reflected in the observed value as y.sub.n.sup.δ, wherein y.sub.n.sup.δy.sub.n+δ, and δ represents a vector with the same dimension as the observed value; transferring the interference information from the observed value to the estimated value of the state variable by the following formula: ${\dot{x}}_n\left(\epsilon ,\delta \right)=\underset{x}{\arg \min }\underset{i=0}{\overset{n}{.Math.}}{F}_n\left({\dot{x}}_n^{-},y_n\right)+\epsilon \left[F\left({\dot{x}}_n^{-},y_n^{\delta }\right)-F\left({\dot{x}}_n^{-},y_n\right)\right]$ wherein ε is a minimum and scalar, and {dot over (x)}.sub.n(ε, δ) represents the estimated value obtained in the case of y.sub.n.sup.δ; and obtaining the estimation accuracy {dot over (x)}.sub.n(ε, δ)−{dot over (x)}.sub.n=Δ{dot over (x)}.sub.n of the state variable based on the formula, wherein Δ{dot over (x)}.sub.n is the deviation of the estimated value from the optimal estimated value; and S4: unitizing the interference information affecting the estimated value, comprising: calculating the partial derivative of {dot over (x)}.sub.n(ε, δ) in the direction ε to obtain a unitized value of the interference information affecting the estimated value: ${{{\frac{d{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}=\frac{d\left[\underset{x_n}{\arg \min }\underset{i=0}{\overset{n}{.Math.}}F\left(x_i,y_i\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}+\frac{d\epsilon \left[F\left({\dot{x}}_n,y_n^{\delta }\right)-F\left(x_n,y_n\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)$ wherein ${\mathscr{H}}_n=\frac{1}{T_1}\underset{i=1}{\overset{T_1}{.Math.}}{\nabla }_{{\dot{x}}_{n-i+1}}^{2}F\left({\dot{x}}_{n-i+1},y_{n-i+1}^{\delta }\right);$ when ∥δ∥.fwdarw.0, simplifying the unitized value of the interference information affecting the estimated value as: ${\frac{{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta ;$ and using the estimation accuracy of the state variable, the simplified unitized value of the interference information affecting the estimated value, and Euler formula to obtain: $\begin{array}{c}{\dot{x}}_n\left(\epsilon ,\delta \right)-{\dot{x}}_n\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\\ \approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta \end{array}$ evaluating the estimation accuracy of energy consumption per ton based on the unitized interference information, comprising: calculating the partial derivative of F({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ) in the direction δ to obtain and unitize the interference information affecting the estimated value under the vision of the evaluation function, and defining the unitized value as an influence function L.sub.n with the specific form as follows:
L.sub.n.sup.T∇.sub.δF({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ).sup.T|.sub.δ=0; simplifying the influence function L.sub.n, and substituting a result of {dot over (x)}.sub.n(ε, δ) into the simplified influence function L.sub.n by using the derivation chain rule to obtain: $\begin{array}{c}{L_n^T\stackrel{△}{=}{\nabla }_{\delta }{F\left({\dot{x}}_n\left(\epsilon ,\delta \right),y_n^{\delta }\right)}^T.Math.}_{\delta =0}\\ {={\nabla }_{{\dot{x}}_n}{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T\frac{d{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\delta }.Math.}_{\delta =0}\\ =-{\nabla }_{{\dot{x}}_n},{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }},{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\end{array}$ wherein ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of F({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction {dot over (x)}.sub.n, and ∇.sub.y.sub.n.sub.δ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction y.sub.n.sup.δ; and obtaining an evaluation result PG.sub.n=f.sub.n(L.sup.i.sub.n) of the estimation accuracy of energy consumption per ton based on a solution formula of the estimation accuracy of the state variable, wherein PG.sub.n represents the evaluation result of energy consumption at time n, and L.sup.i.sub.n represents column i in row i of L.sub.n.sup.T.

2. A system for evaluating estimation accuracy of energy consumption per ton in distillation processes, comprising: a model building module, the model building module being configured to build a state space model of a distillation process, obtain a model predicted value based on the state space model, and obtain an observed value of the distillation process, a method for building the state space model of the distillation process comprising: for a material balance problem, building a high-order model equation for the distillation process, a model with a five-dimensional structure being used, parameters of the distillation process model being changeable within a specific interval, and the concrete model form being:
x.sub.n=A.sub.nx.sub.n−1+E.sub.nu.sub.n+B.sub.nw.sub.n
y.sub.n=C.sub.nx.sub.n+v.sub.n wherein x.sub.n=[x.sub.n.sup.1, x.sub.n.sup.2, x.sub.n.sup.3, x.sub.n.sup.4, x.sub.n.sup.5].sup.T is a state vector of a higher-order model of the distillation process; n is the time; x.sub.n.sup.1 is the mole coefficient of low-density material components at the top in a distillation column; x.sub.n.sup.5 is the mole coefficient of low-density material components at the bottom in the distillation column; x.sub.n.sup.2, x.sub.n.sup.3, x.sub.n.sup.4 are state variables used in estimation of energy consumption per ton; u.sub.n is a controlled variable of a distillation column system; w.sub.n is input disturbance of the distillation column; v.sub.n is sensor disturbance in the distillation column; calculation forms of parameters A.sub.n, B.sub.n and C.sub.n of the distillation column model are: $A_n=\left[\begin{array}{ccccc}-2.9& 0.3& 0& 0& 0\\ 0.9& -1.2& 0.9& 0& 1.1\\ 2.4& 1.5& -4.9& 12.4& 3.2\\ 0& 0& 5.1& 11& 1.1\\ 0& 0& 0& 2.3& -3.9\end{array}\right].$ $B_n={\left[\begin{array}{ccccc}0& 0& 1.6& 0& 0\\ 0& -0.01& 0.02& 0.03& 0\end{array}\right]}^T$ $C_n=I_{1\times 5},I\mathrm{is}a\mathrm{unit}\mathrm{matrix};$ an energy consumption estimation module, the energy consumption estimation module being configured to determine a state estimation model based on the observed value and the model predicted value as follows:
{circumflex over (x)}.sub.n={circumflex over (x)}.sub.n.sup.−+K.sub.n(y.sub.n−C.sub.n{circumflex over (x)}.sub.n.sup.−) wherein n is the time, {circumflex over (x)}.sub.n.sup.− is the model predicted value, y.sub.n is the observed value, {dot over (x)}.sub.n is an estimated value of a state variable, and K.sub.n is an estimated gain; obtain an estimated value of energy consumption per ton in the distillation process according to the state estimation model and the state space model, comprising: conducting state estimation at a certain time according to the state estimation model and the state space model to obtain a state estimated gain and an estimated value of the state variable at a current time; and obtaining an estimated value of energy consumption per ton based on the estimated value of the state variable, a calculation formula being: Ē.sub.n=f(x.sub.n)=1.25[S.sub.1x.sub.n.sup.1−S.sub.2x.sub.n.sup.5]+264.5; an estimation accuracy calculation module, the estimation accuracy calculation module being configured to obtain an estimated value of the state variable with the optimal overall evaluation using a determined evaluation function, comprising: determine a mathematical expression of the evaluation function to be F({dot over (x)}.sub.n.sup.−, y.sub.n), and obtain the estimated value ${\dot{x}}_n=\arg {\min }_{x_n}\frac{1}{T}\underset{i=1}{\overset{T}{.Math.}}F\left({\stackrel{.}{x}}_n^{-},y_n\right)$ of the state variable with the optimal overall evaluation by the evaluation function, wherein T is the duration from an initial time to the current time; describe interference information making the estimated value deviate from a true value and being reflected in the observed value, and transfer the interference information from the observed value to the estimated value of the state variable to obtain an estimation accuracy of the state variable, comprising: represent the interference information making the estimated value deviate from the true value and being reflected in the observed value as y.sub.n.sup.δ, wherein y.sub.n.sup.δy.sub.n+δ, and δ represents a vector with the same dimension as the observed value; transfer the interference information from the observed value to the estimated value of the state variable by the following formula: ${\dot{x}}_n\left(\epsilon ,\delta \right)=\underset{x}{\arg \min }\underset{i=0}{\overset{n}{.Math.}}{F}_n\left({\dot{x}}_n^{-},y_n\right)+\epsilon \left[F\left({\dot{x}}_n^{-},y_n^{\delta }\right)-F\left({\dot{x}}_n^{-},y_n\right)\right]$ wherein ε is a minimum and scalar, and {dot over (x)}.sub.n(ε, δ) represents the estimated value obtained in the case of y.sub.n.sup.δ; and obtain the estimation accuracy {dot over (x)}.sub.n(ε, δ)−{dot over (x)}.sub.n=Δ{dot over (x)}.sub.n of the state variable based on the formula, wherein Δ{dot over (x)}.sub.n is the deviation of the estimated value from the optimal estimated value; and an estimation accuracy evaluation module, the estimation accuracy evaluation module being configured to unitize the interference information affecting the estimated value, and evaluate the estimation accuracy of energy consumption per ton based on the unitized interference information, the estimation accuracy evaluation module comprising an interference information quantization unit, the interference information quantization unit being configured to unitize the interference information affecting the estimated value, by a method comprising: calculating the partial derivative of {dot over (x)}.sub.n(ε, δ) in the direction ε to obtain a unitized value of the interference information affecting the estimated value: ${{{\frac{d{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}=\frac{d\left[\underset{x_n}{\arg \min }\underset{i=0}{\overset{n}{.Math.}}F\left(x_i,y_i\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}+\frac{d\epsilon \left[F\left({\dot{x}}_n,y_n^{\delta }\right)-F\left(x_n,y_n\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)$ wherein ${\mathscr{H}}_n=\frac{1}{T_1}\underset{i=1}{\overset{T_1}{.Math.}}{\nabla }_{{\dot{x}}_{n-i+1}}^{2}F\left({\dot{x}}_{n-i+1},y_{n-i+1}^{\delta }\right);$ when ∥δ∥.fwdarw.0, simplifying the unitized value of the interference information affecting the estimated value as: ${\frac{{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta ;$ using the estimation accuracy of the state variable, the simplified unitized value of the interference information affecting the estimated value, and Euler formula to obtain: ${\frac{{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta$ the estimation accuracy evaluation module comprising an estimation accuracy of energy consumption per ton evaluation unit, the estimation accuracy of energy consumption per ton evaluation unit being configured to evaluate the estimation accuracy of energy consumption per ton based on the unitized interference information, by a method comprising: calculating the partial derivative of F({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ) in the direction δ to obtain and unitize the interference information affecting the estimated value under the vision of the evaluation function, and defining the unitized value as an influence function L.sub.n with the specific form as follows:
L.sub.n.sup.T∇.sub.δF({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ).sup.T|.sub.δ=0; simplifying the influence function L.sub.n, and substituting a result of {dot over (x)}.sub.n(ε, δ) into the simplified influence function L.sub.n by using the derivation chain rule to obtain: $\begin{array}{c}{L_n^T\stackrel{△}{=}{\nabla }_{\delta }{F\left({\dot{x}}_n\left(\epsilon ,\delta \right),y_n^{\delta }\right)}^T.Math.}_{\delta =0}\\ {={\nabla }_{{\dot{x}}_n}{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T\frac{d{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\delta }.Math.}_{\delta =0}\\ =-{\nabla }_{{\dot{x}}_n},{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }},{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\end{array}$ wherein ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of F({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction {dot over (x)}.sub.n, and ∇.sub.y.sub.n.sub.δ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction y.sub.n.sup.δ; and obtaining an evaluation result PG.sub.n=f.sub.n(L.sup.i.sub.n) of the estimation accuracy of energy consumption per ton based on a solution formula of the estimation accuracy of the state variable, wherein PG.sub.n represents the evaluation result of energy consumption at time n, and L.sup.i.sub.n represents column i in row i of L.sub.n.sup.T.

Description

BRIEF DESCRIPTION OF FIGURES

[0050] To illustrate the technical solutions of the examples of the present disclosure more clearly, the accompanying drawings used in the description of the examples are briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples of the present disclosure. For those of ordinary skill in the art, other drawings may also be obtained from these drawings without creative efforts.

[0051] FIG. 1 is a flowchart of a method for evaluating estimation accuracy of energy consumption per ton in distillation processes provided by the present disclosure.

[0052] FIG. 2 is a diagram of an accuracy evaluation result PG.sub.n obtained by a method for evaluating estimation accuracy of energy consumption per ton in distillation processes provided by the present disclosure and an RMSE simulation result of an estimation error (estimated value minus true value) of energy consumption per ton.

[0053] FIG. 3 is a schematic diagram of a system for evaluating estimation accuracy of energy consumption per ton in distillation processes provided by the present disclosure.

[0054] The reference numerals in the accompanying drawings are described as follows: 10. Model building module; 20. Energy consumption estimation module; 30. Estimation accuracy calculation module; and 40. Estimation accuracy evaluation module.

DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of the present disclosure clearer, the embodiments of the present disclosure will be described in further detail below in conjunction with the accompanying drawings.

Example 1

[0056] As shown in FIG. 1, the present example provides a method for evaluating estimation accuracy of energy consumption per ton in distillation processes, which includes the following steps:

[0057] S1: a state space model of a distillation process was built, a model predicted value was obtained based on the state space model, and an observed value of the distillation process was obtained;

[0058] S2: a state estimation model was determined based on the observed value and the model predicted value, and an estimated value of energy consumption per ton in the distillation process was obtained according to the state estimation model and the state space model;

[0059] S3: an estimated value of a state variable with the optimal overall evaluation was obtained using a determined evaluation function, interference information making the estimated value deviate from a true value was extracted from the observed value, and the interference information was transferred from the observed value to the estimated value of the state variable to obtain an estimation accuracy of the state variable; and

[0060] S4: the interference information affecting the estimated value was unitized, and the estimation accuracy of energy consumption per ton was evaluated based on the unitized interference information.

[0061] The present disclosure may well reflect the deviation between the estimated value and the true value without the true value, and evaluate the same object using different estimation methods under the same architecture, so that the evaluation results may cross different estimation methods and still have practicality, and also comparison of the results of evaluation methods of estimation accuracy of energy consumption per ton has practical significance.

[0062] In step S1, a method for building the state space model of the distillation process included:

[0063] firstly, for a material balance problem, a high-order model equation was built for the distillation process, a model with a five-dimensional structure was used, parameters of the distillation process model were changeable within a specific interval, and the concrete model form was:

x.sub.n=A.sub.nx.sub.n−1+E.sub.nu.sub.n+B.sub.nw.sub.n

y.sub.n=C.sub.nx.sub.n+v.sub.n

[0064] where x.sub.n=[x.sub.n.sup.1, x.sub.n.sup.2, x.sub.n.sup.3, x.sub.n.sup.4, x.sub.n.sup.5].sup.T is a state vector of a higher-order model of the distillation process; n is the time; x.sub.n.sup.1 is the mole coefficient of low-density material components at the top in a distillation column; x.sub.n.sup.5 is the mole coefficient of low-density material components at the bottom in the distillation column; generally, low-density materials mainly include gas phase components in a crude oil fractionation process, e.g., gasoline, kerosene and diesel oil; oppositely, high-density materials mainly include liquid phase components in a crude oil separation process, e.g., heavy oil, while the gas phase components and liquid phase components in the distillation column are mixed; therefore, x.sub.n.sup.1 is the mole coefficient of gas phase components in a crude oil mixture at the top in the distillation column, and x.sub.n.sup.5 is the mole coefficient of gas phase components in the crude oil mixture at the bottom in the distillation column; x.sub.n.sup.2, x.sub.n.sup.3, x.sub.n.sup.4 are state variables used in estimation of energy consumption per ton, e.g., temperature, pressure, and reflux ratio; u.sub.n is a controlled variable of a distillation column system, e.g., temperature, and feed valve opening; in the present example, u.sub.n=0, which indicates that there is no control input throughout the operation of the distillation column system (previous studies have shown that the control input in state estimation has no effect on the estimation result, so the controlled variable in the present example was set to zero); w.sub.n is input disturbance of the distillation column; v.sub.n is sensor disturbance in the distillation column; and calculation forms of parameters A.sub.n, B.sub.n and C.sub.n of the distillation column model are:

[00013]$A_n=\left[\begin{array}{ccccc}-2.9& 0.3& 0& 0& 0\\ 0.9& -1.2& 0.9& 0& 1.1\\ 2.4& 1.5& -4.9& 12.4& 3.2\\ 0& 0& 5.1& 11& 1.1\\ 0& 0& 0& 2.3& -3.9\end{array}\right]$$B_n={\left[\begin{array}{ccccc}0& 0& 1.6& 0& 0\\ 0& -0.01& 0.02& 0.03& 0\end{array}\right]}^T$$C_n=I_{1\times 5}\left(I\mathrm{is}a\mathrm{unit}\mathrm{matrix}\right).$

[0065] In step S2, the state estimation model was determined based on the observed value and the model predicted value as follows:

{circumflex over (x)}.sub.n={circumflex over (x)}.sub.n.sup.−+K.sub.n(y.sub.n−C.sub.n{circumflex over (x)}.sub.n.sup.−),

[0066] where n is the time, {circumflex over (x)}.sub.n.sup.− is the model predicted value, y.sub.n is the observed value, {dot over (x)}.sub.n is an estimated value of a state variable, and K.sub.n is an estimated gain.

[0067] Preferably, an unbiased state estimation model was used as the state estimation model in the present example, that is, {dot over (x)}.sub.n was converted into x.sub.n, and the following unbiased state estimation model was built:

x.sub.n=x.sub.n.sup.−+K.sub.n(y.sub.n−C.sub.nx.sub.n.sup.−),

[0068] where n is the time, x.sub.n.sup.− is the model predicted value, y.sub.n is the observed value, x.sub.n is an unbiased estimated value, and K.sub.n is an unbiased estimated gain, x.sub.n.sup.−=A.sub.nx.sub.n−1.

[0069] Refer to the introduction in “Shmaliy, Y. S., Zhao, S., & Ahn, C. K. (2017). Unbiased finite impulse response filtering: an iterative alternative to kalman filtering ignoring noise and initial conditions. IEEE Control Systems Magazine, 37(5), 70-89.” for detailed introduction of the above unbiased state estimation model.

[0070] The unbiased state estimation model calculated the operation state estimation as follows:

[0071] State equations with a time window N m=n−N+1 were collected as follows:

[00014]$\begin{array}{c}\begin{array}{c}\begin{array}{c}\begin{array}{c}\begin{array}{c}{x}_n=A_n{x}_{n-1}+E_n{u}_n+B_n{w}_n\\ x_{n-1}=A_{n-1}{x}_{n-2}+E_{n-1}{u}_{n-1}+B_{n-1}{w}_{n-1}\end{array}\\ .Math.\end{array}\\ x_{m+2}=A_{m+2}{x}_{m+1}+E_{m+2}{u}_{m+2}+B_{m+2}{w}_{m+2}\end{array}\\ x_{m+1}=A_{m+1}{x}_m+E_{m+1}{u}_{m+1}+B_{m+1}{w}_{m+1}\end{array}\\ x_m=x_m+E_m{u}_m+B_m{w}_m\end{array}$

[0072] where m represents the initial time, n represents the current time, and N represents the length of a time window.

[0073] The above equations were combined to obtain an extended state equation (I is a unit matrix):

X.sub.m,n=A.sub.m,nx.sub.m+S.sub.m,nU.sub.m,n+D.sub.m,nW.sub.m,n,

[0074] where X.sub.m,n=[x.sub.m.sup.T, x.sub.m+1.sup.T, . . . , x.sub.n.sup.T].sup.T, U.sub.m,n=[u.sub.m.sup.T, u.sub.m+1.sup.T, . . . , u.sub.n.sup.T].sup.T, W.sub.m,n=[w.sub.m.sup.T, w.sub.m+1.sup.T, . . . , w.sub.n.sup.T].sup.T and A.sub.m,n=[I, A.sub.m+1.sup.T, . . . , (A.sub.n−1.sup.m+1).sup.T, (A.sub.n.sup.m+1).sup.T].sup.T

[00015]$S_{m,n}=\left[\begin{array}{ccccc}{E}_m& 0& .Math.& 0& 0\\ A_{m+1}{E}_m& E_{m+1}& .Math.& 0& 0\\ .Math.& .Math.& \ddots & .Math.& .Math.\\ A_{n-1}^{m+1}{E}_m& A_{n-1}^{m+2}{E}_{m+!}& .Math.& E_{n-1}& 0\\ A_n^{m+1}{E}_m& A_n^{m+2}{E}_{m+!}& .Math.& 0& E_m\end{array}\right],$$D_{m,n}=\left[\begin{array}{ccccc}{B}_m& 0& .Math.& 0& 0\\ A_{m+1}{B}_m& B_{m+1}& .Math.& 0& 0\\ .Math.& .Math.& \ddots & .Math.& .Math.\\ A_{n-1}^{m+1}{B}_m& A_{n-1}^{m+2}{B}_{m+!}& .Math.& B_{n-1}& 0\\ A_n^{m+1}{B}_m& A_n^{m+2}{B}_{m+!}& .Math.& 0& B_m\end{array}\right]$$A_r^g=\{\begin{array}{c}\begin{array}{c}{A}_r{A}_{r-1}.Math.A_g,g<r+1\\ I,g=r+1\end{array}\\ 0,g>r+1\end{array}.$

[0075] Observation equations with a time window N m=n−N+1 were collected as follows:

[00016]$\begin{array}{c}\begin{array}{c}\begin{array}{c}{y}_n=C_n{x}_n+v_n\\ y_{n-1}=C_{n-1}{x}_{n-1}+v_{n-1}\end{array}\\ .Math.\end{array}\\ y_m=C_m{x}_m+v_m\end{array}.$

[0076] The above equations were combined to obtain an extended observation equation:

Y.sub.m,n=H.sub.m,nx.sub.m+L.sub.m,nU.sub.m,n+G.sub.m,nW.sub.m,n+V.sub.m,n,

[0077] where Y.sub.m,n=[y.sub.m.sup.T, y.sub.m+1.sup.T, . . . , y.sub.n.sup.T].sup.T, V.sub.m,n=[v.sub.m.sup.T, v.sub.m+1.sup.T, . . . , v.sub.n.sup.T].sup.T, H.sub.m,n=C.sub.m,nA.sub.m,n, L.sub.m,n=C.sub.m,nS.sub.m,n, G.sub.m,n=C.sub.m,nD.sub.m,n and

[00017]${\stackrel{\_}{C}}_{m,n}=\left[\begin{array}{cccc}{C}_m& 0& .Math.& 0\\ 0& C_{m+1}& .Math.& 0\\ .Math.& .Math.& \ddots & .Math.\\ 0& 0& .Math.& C_m\end{array}\right].$

[0078] The following algorithm was used for iteration from the unbiased initial iteration time l=s s=n−N+z (z is the state dimension) to l=n to obtain an unbiased estimated value x.sub.n and an unbiased gain K.sub.n (the value obtained by iteration to time l=n is the finally desired value). In the present application, z=5.

x.sub.l.sup.−=A.sub.l−1x.sub.l−1+E.sub.lu.sub.l

G.sub.l=[C.sub.l.sup.TC.sub.l+(A.sub.lG.sub.l−1A.sub.l.sup.T).sup.−1].sup.−1

K.sub.l=G.sub.lG.sub.l.sup.T

x.sub.l=x.sub.l.sup.−+K.sub.l(y.sub.l−C.sub.lx.sub.l.sup.−)

[0079] where at the initial value l=s, the calculation formulas were as follows:

G.sub.s=(H.sub.m,s.sup.TH.sub.m,s).sup.−1

x.sub.x=G.sub.sH.sub.m,s.sup.T(Y.sub.m,s−L.sub.m,sU.sub.m,s)+S.sub.m,s.sup.(z)U.sub.m,s

[0080] where z is the state dimension,

[00018]$S_{m,s}^{\left(z\right)}=\underset{z}{\underbrace{\left[A_s^{m+1}{E}_m,A_s^{m+2}{E}_{m+1},.Math.,A_s{E}_{s-1},E_s\right]}}$

[0081] According to the above algorithm process, finally the unbiased estimated value x.sub.n and the unbiased gain K.sub.n at time n were obtained and saved for use.

[0082] An estimated value of energy consumption per ton was obtained based on the estimated value of the state variable, by a calculation formula:

Ē.sub.n=f(x.sub.n)=1.25[S.sub.1x.sub.n.sup.1−S.sub.2x.sub.n.sup.5]+264.5, where S.sub.1=120 ,and S.sub.2=176.

[0083] In step S3, the evaluation function was determined as

F(x.sub.n.sup.−, y.sub.n)=[K.sub.n(y.sub.n−C.sub.nx.sub.n.sup.−)].sup.T[K.sub.n(y.sub.n−C.sub.nx.sub.n.sup.−)], then the overall evaluation function was

[00019]${\stackrel{\_}{x}}_n=\arg {\min }_{x_n}\frac{1}{T}\underset{i=1}{\overset{T}{.Math.}}F\left({\stackrel{\_}{x}}_n^{-},y_n\right).,$

where T is the duration from the initial time to the current time.

[0084] In step S3, a method for extracting the interference information making the estimated value deviate from the true value from the observed value, and transferring the interference information from the observed value to the estimated value of the state variable to obtain the estimation accuracy of the state variable includes:

[0085] The interference information making the estimated value deviate from the true value and being reflected in the observed value was represented as y.sub.n.sup.δ, where y.sub.n.sup.δy.sub.n+δ, and δ represents any unknown vector with the same dimension as the observed value.

[0086] The interference information was transferred from the observed value to the estimated value of the state variable by the following formula:

[00020]${\stackrel{.}{x}}_n\left(\epsilon ,\delta \right)=\underset{x}{\arg \min }\underset{i=0}{\overset{n}{.Math.}}{F}_n\left({\dot{x}}_n^{-},y_n\right)+\epsilon \left[F\left({\dot{x}}_n^{-},y_n^{\delta }\right)-F\left({\stackrel{.}{x}}_n^{-},y_n\right)\right]$

[0087] where ε is a minimum and scalar, and {dot over (x)}.sub.n(ε, δ) represents the estimated value obtained in the case of y.sub.n.sup.δ.

[0088] The estimation accuracy {dot over (x)}.sub.n(ε, δ)−{dot over (x)}.sub.n=Δ{dot over (x)}.sub.n of the state variable was obtained based on the formula, where Δ{dot over (x)}.sub.n is the deviation of the estimated value from the optimal estimated value.

[0089] The partial derivative of {dot over (x)}.sub.n(ε, δ) in the directions ε was calculated to obtain a unitized value of the interference information affecting the estimated value:

[00021]${{{\frac{{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}n=\frac{d\left[\underset{x}{\arg \min }{.Math.}_{i=0}^{n}F\left(x_i,y_i\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}+\frac{d\epsilon \left[F\left({\dot{x}}_n,y_n^{\delta }\right)-F\left(x_n,y_n\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)$

[0090] where

[00022]${\mathscr{H}}_n=\frac{1}{T_1}{\Sigma }_{i=1}^{T_1}{\nabla }_{{\stackrel{.}{x}}_{n-i+1}}^{2}F\left({\stackrel{.}{x}}_{n-i+1},y_{n-i+1}^{\delta }\right).$

[0091] When ∥δ∥.fwdarw.0, the unitized value of the interference information affecting the estimated value was simplified as:

[00023]${\frac{{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta .$

[0092] The estimation accuracy of the state variable, the simplified unitized value of the interference information affecting the estimated value, and Euler formula were used to obtain:

[00024]${\dot{x}}_n\left(\epsilon ,\delta \right)-{\dot{x}}_n\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta$

[0093] In step S4, a method for evaluating the estimation accuracy of energy consumption per ton based on the unitized interference information includes:

[0094] The partial derivative of F({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ) in the direction δ was calculated to obtain and unitize the interference information affecting the estimated value under the vision of the evaluation function, and the unitized value was defined as an influence function L.sub.n with the specific form as follows:

L.sub.n.sup.T∇.sub.δF({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ).sup.T|.sub.δ=0.

[0095] The influence function L.sub.n was simplified, and a result of {dot over (x)}.sub.n(ε, δ) was substituted into the simplified influence function L.sub.n by using the derivation chain rule to obtain:

[00025]$\begin{array}{c}{L_n^T\stackrel{△}{=}{\nabla }_{\delta }{F\left({\dot{x}}_n\left(\epsilon ,\delta \right),y_n^{\delta }\right)}^T.Math.}_{\delta =0}\\ {={\nabla }_{{\dot{x}}_n}{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T\frac{d{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\delta }.Math.}_{\delta =0}\\ =-{\nabla }_{{\dot{x}}_n}{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\end{array}$

[0096] where ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of F({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction {dot over (x)}.sub.n, and ∇.sub.y.sub.n.sub.δ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction y.sub.n.sup.δ.

[0097] An evaluation result PG.sub.n=f.sub.n(L.sup.i.sub.n) of the estimation accuracy of energy consumption per ton was obtained based on a solution formula of the estimation accuracy of the state variable, where PG.sub.n represents the evaluation result of energy consumption at time n, and L.sup.i.sub.n represents column i in row i of L.sub.n.sup.T.

[0098] So far, the evaluation was completed. The evaluation result PG.sub.n of the estimation accuracy of energy consumption per ton obtained based on the above method and an RMSE simulation result of an estimation error (estimated value minus true value) of energy consumption per ton are shown in FIG. 2, where the solid line represents the evaluation result of the estimation accuracy of energy consumption per ton obtained by the method of the present application, and the dotted line represents the actual RMSE simulation result of the estimation error of energy consumption per ton (since the true value of energy consumption per ton cannot be obtained, the true value is obtained by simulation in the present application, and then the RMSE simulation result of the estimation error of energy consumption per ton is obtained). It can be seen that by the method of the present application, the evaluation result of the estimation accuracy of the estimated value of energy consumption per ton obtained by the existing energy consumption per ton estimation method is consistent with change of an estimation error curve of actual energy consumption per ton. It can be seen that the method of the present application can accurately evaluate the estimation accuracy of energy consumption without the true value and the optimal estimated value of energy consumption.

EXAMPLE 2

[0099] As shown in FIG. 3, a system for evaluating estimation accuracy of energy consumption per ton in distillation processes disclosed in Example 2 of the present disclosure is introduced below. The system for evaluating estimation accuracy of energy consumption per ton in distillation processes described in the present example and the method for evaluating estimation accuracy of energy consumption per ton in distillation processes described in Example 1 may be referred to each other.

[0100] The system for evaluating estimation accuracy of energy consumption per ton in distillation processes disclosed by Example 2 includes:

[0101] a model building module 10, the model building module 10 is configured to build a state space model of a distillation process, obtain a model predicted value based on the state space model, and obtain an observed value of the distillation process;

[0102] an energy consumption estimation module 20, the energy consumption estimation module 20 is configured to determine a state estimation model based on the observed value and the model predicted value, and obtain an estimated value of energy consumption per ton in the distillation process according to the state estimation model and the state space model;

[0103] an estimation accuracy calculation module 30, the estimation accuracy calculation module 30 is configured to obtain an estimated value of a state variable with the optimal overall evaluation using a determined evaluation function, describe interference information making the estimated value deviate from a true value and being reflected in the observed value, and transfer the interference information from the observed value to the estimated value of the state variable to obtain an estimation accuracy of the state variable; and

[0104] an estimation accuracy evaluation module 40, the estimation accuracy evaluation module 40 is configured to unitize the interference information affecting the estimated value, and evaluate the estimation accuracy of energy consumption per ton based on the unitized interference information.

[0105] The estimation accuracy evaluation module includes an interference information quantization unit, and the interference information quantization unit is configured to unitize the interference information affecting the estimated value, by a method including:

[0106] The partial derivative of {dot over (x)}.sub.n(ε, δ) in the direction ε was calculated to obtain a unitized value of the interference information affecting the estimated value:

[00026]${{{\frac{{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}n=\frac{d\left[\underset{x}{\arg \min }\stackrel{n}{\underset{i=0}{.Math.}}F\left(x_i,y_i\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}+\frac{d\epsilon \left[F\left({\dot{x}}_n,y_n^{\delta }\right)-F\left(x_n,y_n\right)\right]}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)$

[0107] where

[00027]${\mathscr{H}}_n=\frac{1}{T_1}\underset{i=1}{\overset{T_1}{.Math.}}{\nabla }_{{\dot{x}}_{n-i+1}}^{2}F\left({\dot{x}}_{n-i+1},y_{n-i+1}^{\delta }\right).$

[0108] When ∥δ∥.fwdarw.0, the unitized value of the interference information affecting the estimated value was simplified as:

[00028]${\frac{{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\epsilon }.Math.}_{\epsilon =0}=-{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta .$

[0109] The estimation accuracy of the state variable, the simplified unitized value of the interference information affecting the estimated value, and Euler formula were used to obtain:

[00029]$\begin{array}{c}{\dot{x}}_n\left(\epsilon ,\delta \right)-{\dot{x}}_n\approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)-{\nabla }_{{\dot{x}}_n}F\left(x_n,y_n\right)\right)\\ \approx -{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\delta \end{array}.$

[0110] The estimation accuracy evaluation module includes an estimation accuracy of energy consumption per ton evaluation unit, and the estimation accuracy of energy consumption per ton evaluation unit is configured to evaluate the estimation accuracy of energy consumption per ton based on the unitized interference information, by a method including:

[0111] The partial derivative of F({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ) in the direction δ was calculated to obtain and unitize the interference information affecting the estimated value under the vision of the evaluation function, and the unitized value was defined as an influence function L.sub.n with the specific form as follows:

L.sub.n.sup.T∇.sub.δF({dot over (x)}.sub.n(ε, δ), y.sub.n.sup.δ).sup.T|.sub.δ=0.

[0112] The influence function L.sub.n was simplified, and a result of {dot over (x)}.sub.n(ε, δ) was substituted into the simplified influence function L.sub.n by using the derivation chain rule to obtain:

[00030]$\begin{array}{c}{L_n^T\stackrel{△}{=}{\nabla }_{\delta }{F\left({\dot{x}}_n\left(\epsilon ,\delta \right),y_n^{\delta }\right)}^T.Math.}_{\delta =0}\\ {={\nabla }_{{\dot{x}}_n}{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T\frac{d{\dot{x}}_n\left(\epsilon ,\delta \right)}{d\delta }.Math.}_{\delta =0}\\ =-{\nabla }_{{\dot{x}}_n}{F\left({\dot{x}}_n,y_n^{\delta }\right)}^T{\mathscr{H}}_n^{-1}\left({\nabla }_{y_n^{\delta }}{\nabla }_{{\dot{x}}_n}F\left({\dot{x}}_n,y_n^{\delta }\right)\right)\end{array}$

[0113] where ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of F({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction {dot over (x)}.sub.n, and ∇.sub.y.sub.n.sub.δ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) is the first-order derivative of ∇.sub.{dot over (x)}.sub.nF({dot over (x)}.sub.n, y.sub.n.sup.δ) in the direction y.sub.n.sup.δ.

[0114] An evaluation result PG.sub.n=f.sub.n(L.sup.i.sub.n) of the estimation accuracy of energy consumption per ton was obtained based on a solution formula of the estimation accuracy of the state variable, where PG.sub.n represents the evaluation result of energy consumption at time n, and L.sup.i.sub.n represents column i in row i of L.sub.n.sup.T.

[0115] The system for evaluating estimation accuracy of energy consumption per ton in distillation processes of the present example is used for implementing the aforementioned method for evaluating estimation accuracy of energy consumption per ton in distillation processes. Therefore, the examples of the method for evaluating estimation accuracy of energy consumption per ton in distillation processes described above may be referred to for the specific embodiments of the system, and the description of the corresponding examples may be referred to for the specific embodiments, which will not be introduced here.

[0116] In addition, since the system for evaluating estimation accuracy of energy consumption per ton in distillation processes of the present example is used for implementing the aforementioned method for evaluating estimation accuracy of energy consumption per ton in distillation processes, the effect of the system corresponds to that of the above method, and will not be repeated here.

[0117] Those skilled in the art should understand that the examples of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware example, a complete software example, or an example combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer usable program codes.

[0118] The present application is described with reference to the flowcharts and/or block diagrams of the method, equipment (system), and computer program product according to the examples of the present application. It should be understood that each flow and/or block in the flowcharts and/or block diagrams, and the combination of flows and/or blocks in the flowcharts and/or block diagrams may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing equipment to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing equipment generate a device for implementing a function and/or functions as specified in one or more flows in the flowchart and/or one or more blocks in the block diagram.

[0119] These computer program instructions may also be stored in a computer-readable memory that can guide the computer or other programmable data processing equipment to work in a specific way, such that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements a function and/or functions as specified in one or more flows in the flowchart and/or one or more blocks in the block diagram.

[0120] These computer program instructions may also be loaded onto the computer or other programmable data processing equipment, such that a series of operating steps are executed on the computer or other programmable equipment to generate computer-implemented processing, and instructions executed on the computer or other programmable equipment provide steps for implementing a function and/orfunctions as specified in one or more flows in the flowchart and/or one or more blocks in the block diagram.

[0121] Obviously, the above examples are only examples for clear explanation, not the limitation of embodiments. For those of ordinary skill in the art, other changes or variations in different forms may be made on the basis of the above description. It is unnecessary and impossible to enumerate all embodiments here. The obvious changes or variations arising from the above description are still within the protection scope of the present disclosure.