CN112899496B - Method and system for estimating concentration of copper ions at inlet of zinc hydrometallurgy purification cobalt removal process - Google Patents
Method and system for estimating concentration of copper ions at inlet of zinc hydrometallurgy purification cobalt removal process Download PDFInfo
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Abstract
The invention discloses a method and a system for estimating the concentration of copper ions at an inlet in a wet zinc smelting purification cobalt-removing process, by establishing a first model according to the dynamics principle and the material balance principle of the copper removing process, based on the first model, establishing a second model according to the residence time distribution relationship, establishing a KPLS model of the cobalt removal process, establishing a copper ion concentration estimation model at the inlet of the cobalt removal process based on the first model, the second model and the KPLS model, and the estimated value of the concentration of the copper ions at the inlet of the cobalt removal process is obtained based on the estimated model of the concentration of the copper ions at the inlet of the cobalt removal process, so that the technical problem of low detection precision of the concentration of the copper ions at the inlet of the cobalt removal process in the conventional wet-process zinc smelting purification is solved, the dynamic changes of the concentrations of the copper ions in the copper removal process and the cobalt removal process are fully considered, the concentration of the copper ions at the inlet of the cobalt removal process can be detected in real time on line, the detection precision is high, and guidance is provided for the optimization control of the cobalt removal process.
Description
Technical Field
The invention mainly relates to the technical field of zinc hydrometallurgy, in particular to a method and a system for estimating the concentration of copper ions at an inlet in a zinc hydrometallurgy purification and cobalt removal process.
Background
The purification process is one of the important links in the zinc hydrometallurgy production process, and aims to remove impurity ions in the solution, and can be divided into three sub-processes of copper removal, cobalt removal and cadmium removal according to the difference of impurity ion removal. Copper ions are impurity ions with the highest content and the lowest reduction potential in the zinc sulfate solution and are removed in the first impurity removal process. However, the copper ions also serve as an activator for the subsequent cobalt removal process, so that a certain concentration of copper ions needs to be retained in the solution after copper removal. Cobalt ions are the most difficult of the impurity ions to remove, and are a key parameter influencing the electrolysis efficiency and the product quality, and the concentration of the cobalt ions must be accurately detected.
The cobalt removal process is a complex process comprising three-phase reactions of solid, liquid and gas, wherein the influence factors are numerous. The zinc powder is a main reactant in the cobalt removal process, and the addition amount of the zinc powder directly influences the cobalt removal effect. If the addition of the zinc powder is insufficient, insufficient zinc powder and cobalt ions in the solution do not have oxidation-reduction reaction, and the concentration of the cobalt ions in the solution cannot meet the technical index requirements of the process, so that the product quality is influenced; if the zinc powder is added excessively, the zinc powder is easy to generate oxidation reduction reaction with hydrogen ions in the solution to generate hydrogen, so that the waste of the zinc powder is caused, and the economic cost is increased. The arsenic salt is a catalyst in the cobalt removal process, the cobalt removal efficiency can be obviously improved by adding a proper amount of the arsenic salt, but zinc powder is consumed in the arsenic salt reaction, so that the excessive addition of the arsenic salt can cause the consumption of the zinc powder and inhibit the reduction of cobalt ions; and if too little arsenic salt is added, the cobalt removal effect is difficult to ensure. The cobalt removal process needs to be carried out in a certain pH value range, and if the addition amount of waste acid is insufficient, the pH value is too high, so that the cobalt removal process is hindered; if the waste acid is added in excess, the zinc powder is easy to directly react with hydrogen ions, and the cobalt removal process is also hindered. The cobalt removing process is carried out in a continuous reactor, the average residence time of the solution in the reactor is determined by the size of the liquid flow after copper removing, namely the cobalt removing time is determined, and meanwhile, the larger the liquid flow after copper removing is, the more copper ions enter the reactor in unit time. The proper amount of copper ions is beneficial to the cobalt removal reaction. When the concentration of copper ions in the zinc sulfate solution is too high, the added zinc powder is greatly consumed by the copper ions, so that sufficient zinc powder is not available for cobalt removal, and the cobalt removal efficiency is reduced; the concentration of copper ions is too low, the generated fresh alloy Cu3As is too little, the activation effect on the cobalt removing process is limited, and the cobalt removing efficiency is reduced.
The cobalt removal process belongs to oxidation-reduction reaction, and the oxidation-reduction degree in the reactor can be reflected by oxidation-reduction potential ORP. In an actual industrial field, the ORP can be detected in real time, and the ORP is controlled within a certain range by adjusting the blanking amount of zinc powder, arsenic salt and waste acid flow, so that the blindness of adding reactants in the cobalt removal process is overcome to a great extent.
As a downstream process of the decoppering process, the fluctuation change of the concentration of copper ions at the inlet of the decoppering process is large, but because the field production environment is severe, an online detection device for the concentration of copper ions which can stably operate on the field for a long time is lacked, the acquisition of the information of the concentration of copper ions at the inlet of the decoppering process is delayed by adopting a manual sampling and testing mode, and the detection precision is low, so that the blindness of zinc powder addition is increased, and the difficulty is brought to the stable operation of the decoppering process.
Disclosure of Invention
The method and the system for estimating the concentration of the copper ions at the inlet of the wet-process zinc smelting purification and cobalt removal process solve the technical problem of low detection precision of the concentration of the copper ions at the inlet of the existing wet-process zinc smelting purification and cobalt removal process.
In order to solve the technical problem, the method for estimating the concentration of the copper ions at the inlet of the wet zinc smelting purification cobalt removal process comprises the following steps:
establishing a first model according to a dynamics principle and a material balance principle of a copper removal process, wherein the first model is a relation model of an oxidation-reduction potential and copper ion concentration of the copper-removed liquid in the copper removal process;
establishing a second model according to the residence time distribution relation based on the first model, wherein the second model is a relation model of the concentration of copper ions in the decoppered liquid and the concentration of copper ions in the decoppered overflow;
establishing a KPLS model of a cobalt removal process;
and establishing a cobalt removal process inlet copper ion concentration estimation model based on the first model, the second model and the KPLS model, and obtaining a cobalt removal process inlet copper ion concentration estimation value based on the cobalt removal process inlet copper ion concentration estimation model.
Further, according to the dynamics principle and the material balance principle of the copper removal process, establishing a first model comprises the following steps:
obtaining the relation between the activation energy and the potential based on the electrode reaction kinetics principle;
obtaining a chemical reaction constant based on an electrochemical reaction principle according to the relation between the activation energy and the potential;
obtaining a reaction rate based on a first-order chemical reaction in the copper removal process according to a chemical reaction constant and the concentration of copper ions at the outlet of the copper removal reactor;
establishing a first model based on a material balance principle according to a reaction rate, wherein the first model specifically comprises the following steps:
wherein the content of the first and second substances,
C1(t) represents the copper ion concentration of the solution after copper removal at time t,
represents the copper ion concentration at the inlet of the copper removal reactor, Q represents the flow rate of the zinc sulfate solution in the copper removal process, V represents the volume of the copper removal reactor, t0Represents the initial time, AβRepresents a pre-exponential factor, EeRepresents the standard activation energy parameter, alpha represents the proportionality coefficient, F represents the Faraday constant, eorpRepresents a potential value, eeqRepresents the standard equilibrium potential of copper ions, R represents the gas constant, and T represents the Kelvin temperature.
Further, based on the first model, establishing the second model according to the residence time distribution relationship includes:
acquiring the probability that the fluid element entering the copper removal overflow groove in the previous n hours is still left in the overflow groove at the time t;
based on the first model, according to the probability, establishing a second model, wherein the second model specifically comprises the following steps:
wherein C is2(t) represents the copper ion concentration of the copper removal overflow at time t, C1(T-i. DELTA.T) representsThe copper ion concentration of the copper-removed liquid is within the time interval delta T from the current time T to the previous i hours, i represents the ith small time interval in the previous n small time intervals, V2And the volume of the copper removal overflow groove is expressed, DeltaT represents the time length of each short section of time, Q (T-i DeltaT) represents the flow rate of the copper removal liquid in the time interval DeltaT from the current time T to the previous i hours, and E (i DeltaT) represents the residence time probability of the copper removal liquid in the time interval DeltaT from the current time T to the previous i hours.
Further, establishing a KPLS model of the cobalt removal process comprises:
selecting the blanking amount of a zinc powder bin, the flow rate of a cobalt removal inlet solution, the additive amount of waste acid, the additive amount of arsenic salt and the oxidation-reduction potential of a cobalt removal reactor in the cobalt removal process to construct an input matrix of a KPLS (kernel nearest neighbor) model, and selecting the concentration variable of copper ions of copper removal overflow to construct an output matrix;
normalizing the input matrix and the output matrix;
introducing a Gaussian kernel function to map the input matrix to a high-dimensional feature space, obtaining a feature space matrix of the input matrix, and performing centralization processing on the feature space matrix;
establishing a KPLS model based on the feature space matrix after the centralization processing, wherein the KPLS model specifically comprises the following steps:
Y＝K0B+R
wherein K0Representing the feature space matrix after centralization, Y representing the output matrix, T and U respectively representing K0And a scoring matrix of Y, P and Q each representing K0And Y, E and F each represent K0And a residual matrix of Y, wherein A is the number of hidden variables reserved in the model, B and R are a regression coefficient matrix and a residual matrix, respectively
T＝[t1,t2,...tA]，U＝[u1,u2,...uA]，P＝[p1,p2,...pA]，Q＝[q1,q2,...qA]。
Further, based on the first model, the second model and the KPLS model, establishing the cobalt removal process inlet copper ion concentration estimation model comprises:
obtaining a model coordination factor through online detection data of a copper removal process and fuzzy reasoning;
establishing a cobalt removal process inlet copper ion concentration estimation model according to the first model, the second model, the KPLS model and the model coordination factor, wherein the cobalt removal process inlet copper ion concentration estimation model is specifically;
Ccombine＝μC2+(1-μ)Y，
wherein, CcombineEstimation model of copper ion concentration at inlet for cobalt removal process, C2The concentration of copper ions in the copper removal overflow calculated according to the second model, Y is the concentration of copper ions in the copper removal overflow calculated according to the KPLS model, and mu is a model coordination factor.
Further, the step of obtaining the model coordination factor through the online detection data of the copper removal process by fuzzy reasoning comprises the following steps:
selecting the variable quantity of the zinc powder as a first input variable, the variable quantity of the potential as a second input variable and the model coordination factor as an output variable;
determining a fuzzy set of a first input variable, a second input variable and an output variable according to the membership function;
and obtaining the model coordination factor by adopting a gravity center method according to the fuzzy set of the first input variable, the second input variable and the output variable based on the self-defined model coordination factor fuzzy rule.
Further, determining the fuzzy sets of the first input variable, the second input variable and the output variable according to the membership function specifically includes:
determining a first fuzzy set of a first input variable, a second input variable and an output variable according to the Z-type membership function;
determining a first input variable, a second input variable and a second fuzzy set, a third fuzzy set and a fourth fuzzy set of output variables according to the bell-type membership function;
and determining a fifth fuzzy set of the first input variable, the second input variable and the output variable according to the S-type membership function.
The invention provides a system for estimating the concentration of copper ions at an inlet in the process of purifying and removing cobalt by zinc hydrometallurgy, which comprises:
the device comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor realizes the steps of the method for estimating the concentration of the copper ions at the inlet of the wet zinc smelting purification cobalt removal process when executing the computer program.
Compared with the prior art, the invention has the advantages that:
the invention provides a method and a system for estimating the concentration of copper ions at an inlet of a wet zinc smelting purification and decobalting process, which solve the technical problem of low detection precision of the concentration of the copper ions at the inlet of the existing wet zinc smelting purification and decobalting process by establishing a first model according to the dynamics principle of the decobalting process and the material balance principle, establishing a second model according to the residence time distribution relation based on the first model, establishing a second model which is a relation model between the concentration of the copper ions in the decobalting process and the concentration of copper ions overflowing from the decobalting process, establishing a KPLS model of the decobalting process and establishing an estimation model of the concentration of the copper ions at the inlet of the decobalting process based on the first model, the second model and the KPLS model, the dynamic change of the copper ion concentration in the copper removing process and the cobalt removing process is fully considered, the concentration of the copper ions at the inlet of the cobalt removing process can be detected on line in real time, the detection precision is high, and guidance is provided for the optimization control of the cobalt removing process.
Drawings
FIG. 1 is a flowchart of a method for estimating the concentration of copper ions at an inlet of a wet zinc metallurgy purification cobalt-removing process according to a first embodiment of the present invention;
FIG. 2 is a process flow diagram of a zinc hydrometallurgy cobalt removal process in accordance with the second embodiment of the present invention;
FIG. 3 is a flowchart of a method for estimating the concentration of copper ions at the inlet of a wet zinc metallurgy purification cobalt-removing process according to a second embodiment of the present invention;
FIG. 4 is a diagram illustrating a relationship of Δ zinc membership functions according to a second embodiment of the present invention;
FIG. 5 is a Δ ORP membership function according to example two of the present invention;
FIG. 6 is a mu membership function according to a second embodiment of the present invention;
FIG. 7 is a block diagram of the second embodiment of the present invention, which is a structural diagram of a method for estimating the concentration of copper ions at the inlet of a cobalt removal process based on associated process information;
FIG. 8 is a comparison graph of the estimated copper ion concentration value and the actual copper ion concentration value in example two of the present invention;
fig. 9 is a block diagram of a system for estimating the concentration of copper ions at the inlet of a hydrometallurgy zinc purification cobalt removal process according to an embodiment of the present invention.
Reference numerals:
10. a memory; 20. a processor.
Detailed Description
In order to facilitate an understanding of the invention, the invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of the invention is not limited to the specific embodiments below.
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example one
Referring to fig. 1, a method for estimating the concentration of copper ions at an inlet of a wet zinc smelting purification cobalt removal process provided by an embodiment of the present invention includes:
and S101, establishing a first model according to a dynamics principle and a material balance principle of a copper removal process, wherein the first model is a relation model of an oxidation-reduction potential and copper ion concentration of the copper-removed liquid in the copper removal process.
And S102, establishing a second model according to the residence time distribution relation based on the first model, wherein the second model is a relation model of the concentration of copper ions in the decoppered liquid and the concentration of copper ions in the decoppered overflow.
And step S103, establishing a KPLS model in the cobalt removal process.
And step S104, establishing a cobalt removal process inlet copper ion concentration estimation model based on the first model, the second model and the KPLS model, and obtaining a cobalt removal process inlet copper ion concentration estimation value based on the cobalt removal process inlet copper ion concentration estimation model.
The method for estimating the concentration of copper ions at the inlet of the wet zinc smelting purification and cobalt removal process, provided by the embodiment of the invention, comprises the steps of establishing a first model according to the dynamics principle of the copper removal process and the material balance principle, wherein the first model is a relation model of the oxidation-reduction potential of the copper removal process and the concentration of copper ions in liquid after copper removal, establishing a second model according to the residence time distribution relation based on the first model, the second model is a relation model of the concentration of copper ions in liquid after copper removal and the concentration of copper ions overflowing during copper removal, establishing a KPLS model of the cobalt removal process, establishing an estimation model of the concentration of copper ions at the inlet of the cobalt removal process based on the first model, the second model and the KPLS model, and obtaining an estimation value of the concentration of copper ions at the inlet of the cobalt removal process based on the estimation model of the concentration of copper ions at the inlet of the zinc smelting purification and cobalt removal process by a wet method, so as to solve the technical problem of low detection precision of the concentration of copper ions at the inlet of the zinc smelting purification and cobalt removal process by a wet method, the dynamic change of the copper ion concentration in the copper removing process and the cobalt removing process is fully considered, the concentration of the copper ions at the inlet of the cobalt removing process can be detected on line in real time, the detection precision is high, and guidance is provided for the optimization control of the cobalt removing process.
Because the concentration of the copper ions at the inlet of the cobalt removing process depends on the copper removing effect of the copper removing process, and the online detection data of the cobalt removing process can well reflect the change of the concentration of the copper ions at the inlet in the cobalt removing process, the copper ion concentration estimation model at the inlet of the cobalt removing process is established by combining the dynamic change of the concentration of the copper ions in the copper removing process and the cobalt removing process, so that the high-precision estimated value of the concentration of the copper ions at the inlet of the cobalt removing process can be obtained.
Example two
Taking a zinc hydrometallurgy purification process of a certain smelting plant as an example, the process flow chart is shown in fig. 2, the copper removal process is mainly carried out in two continuous stirring reactors, and copper ions in neutral supernatant are precipitated in the form of cuprous oxide by adding zinc powder into the reactors. And (3) carrying out solid-liquid separation on the zinc sulfate solution subjected to copper removal in a thickener, returning the settled underflow part to the No. 1 copper removal reactor, sending the overflow of the thickener to a copper removal overflow tank, and sending the overflow solution of the copper removal overflow tank to a cobalt removal process. The purification and cobalt removal process is mainly carried out in four continuous stirred reactors, and cobalt ions are gradually reacted in the four reactors by adding zinc powder, arsenic salt and waste acid into the four reactors, so that the cobalt ion removal effect is achieved. The cobalt alloy is precipitated in the thickener, the crystal seeds which are formed and are beneficial to the cobalt removal reaction are returned to the reactor No. 1, and the overflow of the thickener is sent to the subsequent working section. The invention aims to realize the real-time estimation of the concentration of copper ions at the inlet (copper removal overflow) of the cobalt removal process.
Referring to fig. 3, the method for estimating the concentration of copper ions at the inlet of the wet zinc smelting purification cobalt removal process provided by the second embodiment of the present invention includes:
step S201, a first model is established according to a dynamics principle and a material balance principle of a copper removal process, and the first model is a relation model of an oxidation-reduction potential and copper ion concentration of the copper-removed liquid in the copper removal process.
Specifically, in this embodiment, according to the principle of dynamics of the copper removal process and the principle of material balance, establishing the first model includes:
step S2011: the reaction of the copper removal process is a first-order chemical reaction, and the reaction rate can be expressed as:
wherein r is the chemical reaction rate, k is the reaction constant,
the copper ion concentration of the reactor is the copper removal.
Step S2012: according to the principles of electrochemical reactions, the arrhenius equation is used to describe the relationship between chemical reaction constants and activation energies:
in the formula, AβIs a pre-exponential factor, EaFor activation energy, R is the gas constant and T is the Kelvin temperature.
Step S2013: from the analysis of electrode reaction kinetics, the relationship between activation energy and potential:
Ea＝Ee+2αF(eorp-eeq) (3)
wherein F is the Faraday constant, EeAs a standard activation energy parameter, eeqIs the standard equilibrium potential of copper ions, eorpAlpha is a proportionality coefficient.
Step S2014: combining formulas (1), (2) and (3) to obtain:
step S2015: the principle of material balance in the process industry is expressed as follows: the amount of change in the reactant is the amount of inflow of the reactant-the amount of outflow of the reactant-the reaction amount of the reactant. According to the principle of material balance, the method comprises the following steps:
wherein V is the volume of the copper removing reactor,
in order to remove the copper ion concentration of the inlet of the copper reactor,
for the copper ion concentration at the outlet of the copper removal reactor, Q is the flow rate of the zinc sulfate solution in the copper removal process。
Order to
Therefore, the above formula can be formed:
solving the above equation yields:
wherein the content of the first and second substances,
C1(t) represents the copper ion concentration of the solution after copper removal at time t,
represents the copper ion concentration at the inlet of the copper removal reactor, Q represents the flow rate of the zinc sulfate solution in the copper removal process, V represents the volume of the copper removal reactor, t0Represents the initial time, AβRepresents a pre-exponential factor, EeRepresents the standard activation energy parameter, alpha represents the proportionality coefficient, F represents the Faraday constant, eorpRepresents a potential value, eeqRepresents the standard equilibrium potential of copper ions, R represents the gas constant, and T represents the Kelvin temperature.
And S202, establishing a second model according to the residence time distribution relation based on the first model, wherein the second model is a relation model of the concentration of copper ions in the decoppered liquid and the concentration of copper ions in the decoppered overflow.
Specifically, the embodiment is based on a first model, and a second model is established according to the residence time distribution relationship, where the second model is a relationship model of copper ion concentration of the copper-removed liquid and copper ion concentration of the copper-removal overflow, and the relationship model includes:
step S2021: in a practical industrial reactor, the total residence time of a particle from entry to exit the reactor is described by the residence time, characterized by probability using the residence time probability density function:
wherein N is the number of reactors, V2For the volume of the vessel, Q is the flow rate of the flow.
Step S2022: considering that the solution entering the copper removal overflow tank in each short time interval DeltaT is regarded as a fluid element, the formula (7) is satisfied for each fluid element entering the copper removal overflow tank, and for the current time T, the flow rate of the solution entering the copper removal overflow tank in the first n small time intervals can be represented as follows:
Q(t-n△T)△T,Q(t-(n-1)△T)△T,...,Q(t-△T)△T (9)
wherein Q (t) is the flow rate of the solution entering the copper removal overflow tank.
For the current time t, the concentration of the solution entering the copper removal overflow tank in the first n small time periods can be expressed as: c1(t-n△T),C1(t-(n-1)△T),...,C1(t-△T)
Step S2023: according to the formula (8), the probability that the fluid element entering the copper removal overflow groove in the previous n hours remains in the overflow groove at the time t is respectively as follows:
E(n△T),E((n-1)△T),…,E(△T) (10)
then the concentration of the copper ions at the outlet of the copper removal overflow groove at the moment t can be obtained as follows:
in the formula, C2(t) represents the copper ion concentration of the copper removal overflow at time t, C1(T-i delta T) represents the copper ion concentration of the copper-removed liquid in a time period delta T from the current time T to the previous i hours, i represents the ith hour time period in the previous n hour time periods, V2The volume of the copper removal overflow groove is shown, DeltaT represents the time length of each short period, Q (T-i DeltaT) represents the copper removal liquid of the time interval DeltaT from the current time T to the previous time iE (i Δ T) represents the probability of dwell time Δ T for the current time T for the period of i hours preceding it.
Step S2024: the method comprises the following steps of utilizing a large amount of historical data (flow, ion concentration, potential and the like) generated by field operation to carry out parameter identification on a relational model by adopting a particle swarm algorithm to obtain parameters, wherein the particle swarm algorithm is a commonly used technical method in parameter identification, so that the implementation process is not described in detail, and the method uses the minimum sum of squares of errors of a model output value and a true value as an optimization target to carry out parameter identification:
in the formula, N is the number C of samples1,iThe true value of the copper ion concentration of the ith sample of the solution after copper removal, C1,k,iCalculated value of copper ion concentration of ith sample of solution after copper removal, C2,iTrue value of copper ion concentration of ith sample for copper removal overflow, C2,k,iThe copper ion concentration of the ith sample was calculated for the copper removal overflow.
Step S203, selecting the blanking amount of a zinc powder bin of a cobalt removal reactor at the inlet of the cobalt removal process, the solution flow rate at the cobalt removal inlet, the addition amount of waste acid, the addition amount of arsenic salt and the oxidation-reduction potential to construct an input matrix of a KPLS (kernel partial least squares) model, selecting the copper ion concentration variable of copper removal overflow to construct an output matrix, and normalizing the input matrix and the output matrix.
Specifically, the input matrix X and the output matrix Y are normalized. Wherein the input matrix X is composed of the variable parameters of Table 1, and the output matrix Y is the copper ion concentration of the copper removal overflow.
TABLE 1 cobalt removal Process on-line test data
| Serial number | Parameter(s) | Unit of |
| 1 | 1# zinc powder bin blanking amount | kg/h |
| 2 | Cobalt removal inlet solution flow rate | m3/h |
| 3 | Addition amount of 1# waste acid | m3/h |
| 4 | 1# arsenic salt addition | L/h |
| 5 | 1# Oxidation reduction potential | mV |
And step S204, introducing a Gaussian kernel function to map the input matrix to a high-dimensional feature space, obtaining a feature space matrix of the input matrix, and performing centralization processing on the feature space matrix.
The method comprises the following specific steps:
step 1: the matrix K is calculated.
Ki,j＝k(xi,xj) (13)
In the formula, xi，xjI and j input samples in the input matrix X, k (-) is the kernel functionNumber, Ki,jIs the element in the ith row and the jth column of the matrix K. The invention selects and uses Gaussian kernel function
Where σ is 4.
Step 2: centering the matrix K:
in the formula, I is an n multiplied by n unit matrix, and s is a column vector of n dimension 1 columns and all 1 columns;
a transposed matrix for s; n is the number of samples in the input matrix X.
Step S205, a KPLS model is established based on the feature space matrix after the centralization processing, and the KPLS model specifically comprises:
wherein K0Representing the feature space matrix after centralization, Y representing the output matrix, T and U respectively representing K0And a scoring matrix of Y, P and Q each representing K0And Y, E and F each represent K0And a residual matrix of Y, wherein A is the number of hidden variables reserved in the model, B and R are a regression coefficient matrix and a residual matrix, respectively
T＝[t1,t2,...tA]，U＝[u1,u2,...uA]，P＝[p1,p2,...pA]，Q＝[q1,q2,...qA]。
Further, in this embodiment, the number of principal elements is determined by using a cross-validation method, and a regression coefficient matrix B is solved. The method comprises the following specific steps:
step 1: initialization ui；
Step 2: calculating a score matrix of the input matrix: t is ti＝Kui；
Step 3: will score the matrix tiCarrying out normalization processing;
step 4: calculating a load matrix of the output matrix:
step 5: and (3) calculating a load principal element: u. ofi＝Yqi；
Step 6: will matrix uiCarrying out normalization processing;
step 7: judgment uiWhether to converge or not: jump to step8 if convergence; if not, jump to step 2;
step 8: updating the matrix:
wherein I is an identity matrix;
step 9: and i is i +1, judging whether i is larger than A: if yes, ending; if not, jump to step 2;
step 10: calculating regression coefficients
In addition, the predicted value of the test data in this embodiment is:
in the formula, XtIs a matrix of test data, KtIs a data matrix XtThe feature space matrix of (2).
Specifically, the oxidation-reduction potential ORP in the cobalt removing process is comprehensively determined by all factors such as added zinc powder, arsenic salt, waste acid, flow, inlet copper ion concentration and the like, in fact, theoretically, ORP (zinc powder, arsenic salt, waste acid, flow and inlet copper ion concentration) should be f, and the zinc powder, arsenic salt, waste acid, flow and potential ORP can be detected on line in real time on the actual industrial site, but the inlet copper ion concentration needs to be sampled and detected by an operator (the test interval is 2 hours), this example therefore estimates the inlet copper ion concentration for a long assay interval based on variables (zinc dust, arsenic salts, spent acid, flow rate, potential ORP) that can be detected in real time in the field, namely, the inlet copper ion concentration f (zinc powder, arsenic salt, waste acid, flow and ORP) is obtained, so that the online accurate estimation of the cobalt-removing inlet copper ion concentration is realized, and guidance is provided for the optimization control of the cobalt-removing process.
And step S206, obtaining a model coordination factor through fuzzy reasoning according to the online detection data of the copper removal process.
Further, in this embodiment, the obtaining of the model coordination factor through the online detection data of the copper removal process by fuzzy inference includes: selecting the variable quantity of the zinc powder as a first input variable, the variable quantity of the potential as a second input variable and the model coordination factor as an output variable, determining a fuzzy set of the first input variable, the second input variable and the output variable according to a membership function, and obtaining the model coordination factor by adopting a gravity center method according to the fuzzy set of the first input variable, the second input variable and the output variable on the basis of a self-defined model coordination factor fuzzy rule.
Specifically, the model coordination factor μ of the present embodiment represents the proportion of the copper removal mechanism model when the two models are integrated, and when the mechanism model formula is used to calculate the copper ion concentration, the copper ion concentration at the copper removal inlet is determined
The assay interval of (2) is 2 hours, so this formula calculates the inlet copper ion concentration
The last assay value is used and defaults to a constant or non-varying concentration of copper ions at the inlet. However, in an actual industrial field, the fluctuation of the copper ion concentration at an inlet is sometimes large, so that the copper ion concentration cannot be well predicted by using a copper removal mechanism model alone. In the removal of cobaltAnd a step of rapidly fluctuating the copper ion concentration at the reaction inlet by the oxidation-reduction potential ORP when the fluctuation of the copper ion concentration at the cobalt removal inlet is large. Therefore, in the present embodiment, the model coordination factor μ is increased when the fluctuation of the copper ion concentration is small, and the model coordination factor μ is appropriately decreased when the fluctuation of the copper ion concentration is large, so as to increase the proportion of the cobalt removal data model. The model coordination factor mu of the embodiment is obtained by fuzzy reasoning of online detection data of the copper removal process. The method comprises the following specific steps:
in step S2061, the variation quantity delta zinc of the first input variable zinc powder, the variation quantity delta ORP of the second input variable potential and the output variable are model coordination factors mu. The change amount Δ zinc of the zinc powder and the change amount Δ ORP of the potential are expressed as follows:
in the formula, zinck、zinck-T、ORPk、ORPk-TThe addition amount of zinc powder at the current time k, the addition amount of zinc powder at the time T before, the oxidation-reduction potential value at the current time k, and the oxidation-reduction potential value at the time T before are respectively shown, wherein T is 10 min.
Step S2062, according to the characteristics of the copper removing process, dividing the Delta zinc into 5 fuzzy sets: NB (negative large), NS (negative small), ZO (normal), PS (positive small), PB (positive large). ZO represents that the value of the current zinc powder change variable is within the process index range, NS represents that the current zinc powder addition amount is in a slow-down state, NB represents that the current zinc powder addition amount is in a fast-down state, PS represents that the current zinc powder addition amount is in a slow-up state, and PB represents that the current zinc powder addition amount is in a fast-up state.
Divide Δ ORP into 5 fuzzy sets: NB (negative large), NS (negative small), ZO (normal), PS (positive small), PB (positive large). ZO represents that the current oxidation-reduction potential value ORP is in the process index range, NS represents that the current oxidation-reduction potential value ORP is in a slow descending state, NB represents that the current oxidation-reduction potential value ORP is in a fast descending state, PS represents that the current oxidation-reduction potential value ORP is in a slow ascending state, and PB represents that the current oxidation-reduction potential value ORP is in a fast ascending state.
The model coordination factor μ was divided into 5 fuzzy sets: NB (small proportion of mechanism model), NS (small proportion of mechanism model), ZO (moderate proportion of mechanism model), PS (large proportion of mechanism model) and PB (large proportion of mechanism model).
Step S2063, respectively establishing a first membership function of the first input variable Δ zinc, a second membership function of the second input variable Δ zinc, and a third membership function of the output variable μ, as shown in fig. 4, 5, and 6.
The first fuzzy set (NB) of Δ zinc, Δ ORP, and μ is determined by a Z-type membership function:
in the formula, α and β determine the slope of the function curve.
The middle three (NS, ZO, PS) fuzzy sets of Δ zinc, Δ ORP, and μ are determined by the bell-type membership functions:
in the formula, a and b determine the shape of the function, and c determines the position of the function curve.
The last fuzzy set (PB) of Δ zinc, Δ ORP and μ is determined by the S-type membership function:
in the formula, σ and ω determine the slope of the function curve.
According to the field expert experience and the analysis of the data, the parameters of each membership function are shown in the table 2:
TABLE 2
Step S2064, creating a model coordination factor fuzzy rule based on the zinc powder and the variation of the electric potential according to the obtained fuzzy set, where the rule matrix table is shown in table 3:
TABLE 3
Rule one is as follows: when the amount of change in zinc powder is within a suitable range, there is a slight tendency for the oxidation-reduction potential ORP to rise or fall or to be substantially constant, which is considered to be a case where the fluctuation in the copper ion concentration is not large, and the coordination factor takes a moderate value. The rule is as follows:
rule two: when the change amount of the zinc powder has a great descending trend, the oxidation-reduction potential ORP is basically unchanged; when the change amount of the zinc powder has a slight descending tendency, the oxidation-reduction potential ORP has a slight descending tendency or is basically unchanged; when the amount of change of the zinc powder is within a suitable range, the oxidation-reduction potential ORP has a slight tendency to rise or fall or is substantially unchanged; when the change amount of the zinc powder has a slight rising tendency, the oxidation-reduction potential ORP has a slight rising tendency or is substantially unchanged; when the change amount of zinc powder has a great rising trend, the oxidation-reduction potential ORP is basically unchanged, which is considered that the concentration fluctuation of copper ions is great, and the coordination factor takes a small value. The rule is as follows:
rule three: when the change amount of the zinc powder has a great descending trend, the oxidation-reduction potential ORP has a great or slight descending trend; when the change amount of the zinc powder has a slight descending trend, the oxidation-reduction potential ORP has a great descending trend; when the change amount of the zinc powder has a slight rising trend, the oxidation-reduction potential ORP has a larger rising trend; when the amount of change of zinc powder has a large rising tendency, the oxidation-reduction potential ORP has a large or slight rising tendency, which is considered that the copper ion concentration fluctuates greatly and the coordination factor takes a small value. The rule is as follows:
rule four: when the change amount of the zinc powder has a slight descending trend, the oxidation-reduction potential ORP has a slight or great ascending trend; when the change amount of zinc powder has a slight upward trend, the oxidation-reduction potential ORP has a slight or great downward trend, which is considered that the copper ion concentration is relatively stable, and the coordination factor takes a relatively large value. The rule is as follows:
rule five: when the change amount of the zinc powder has a great descending trend, the oxidation-reduction potential ORP has a slight or great descending trend; when the change amount of zinc powder has a large rising tendency, the oxidation-reduction potential ORP has a slight or large rising tendency, which is considered to be stable at the time of copper ion concentration, and the coordination factor takes a large value. The rule is as follows:
and S2065, converting the fuzzy result value into a corresponding numerical value by adopting a gravity center method to obtain the coordination factor mu.
Step S207, establishing a cobalt removal process inlet copper ion concentration estimation model according to the first model, the second model, the KPLS model and the model coordination factor, and obtaining a cobalt removal process inlet copper ion concentration estimation value based on the cobalt removal process inlet copper ion concentration estimation model, wherein the cobalt removal process inlet copper ion concentration estimation model is specifically shown in the specification;
Ccombine＝μC2+(1-μ)Y (26)
wherein, CcombineEstimation model of copper ion concentration at inlet for cobalt removal process, C2The concentration of copper ions in the copper removal overflow calculated according to the second model, Y is the concentration of copper ions in the copper removal overflow calculated according to the KPLS model, and mu is a model coordination factor.
The method for estimating the concentration of copper ions at the inlet of the wet zinc smelting purification and cobalt removal process, provided by the embodiment of the invention, comprises the steps of establishing a first model according to the dynamics principle of the copper removal process and the material balance principle, wherein the first model is a relation model of the oxidation-reduction potential of the copper removal process and the concentration of copper ions in liquid after copper removal, establishing a second model according to the residence time distribution relation based on the first model, the second model is a relation model of the concentration of copper ions in liquid after copper removal and the concentration of copper ions overflowing during copper removal, establishing a KPLS model of the cobalt removal process, establishing an estimation model of the concentration of copper ions at the inlet of the cobalt removal process based on the first model, the second model and the KPLS model, and obtaining an estimation value of the concentration of copper ions at the inlet of the cobalt removal process based on the estimation model of the concentration of copper ions at the inlet of the zinc smelting purification and cobalt removal process by a wet method, so as to solve the technical problem of low detection precision of the concentration of copper ions at the inlet of the zinc smelting purification and cobalt removal process by a wet method, the dynamic change of the copper ion concentration in the copper removing process and the cobalt removing process is fully considered, the concentration of the copper ions at the inlet of the cobalt removing process can be detected on line in real time, the detection precision is high, and guidance is provided for the optimization control of the cobalt removing process.
Specifically, if the estimation effect of the copper ion concentration at the inlet of the cobalt removing process is not good only by using the output of the previous copper removing process, because it can be known from the mechanism model (formula 5) of the copper removing process that the copper ion concentration at the outlet of the copper removing reactor is determined by the copper ion concentration at the inlet and the reacted copper ions, according to the parameters (e) such as the potential, temperature, etc. detected on lineorpT) can characterize the reacted copper ions in real time, but the concentration information of the copper ions at the inlet
But is a test value of once every two hours, so thatWhen the above formula is used, real-time copper ion concentration information of the inlet cannot be obtained, and only the copper ion concentration of the inlet is assumed to be unchanged within the two hours, but in an actual field, the copper ion concentration of the inlet is often fluctuated greatly, so that the copper ion concentration of the outlet of the mechanism model is caused due to inaccurate copper ion concentration information of the inlet
Inaccurate and limited. In the cobalt removal process, the potential ORP is sensitive to the concentration change of the copper ions at the inlet and can well reflect the concentration change of the copper ions at the inlet, so that the online detection data of the cobalt removal process is used in the embodiment to evaluate the concentration change condition of the copper ions at the inlet in real time. That is, the present embodiment fully considers the dynamic changes of the copper ion concentration in the copper removal process and the cobalt removal process, and can establish a high-precision estimation model of the copper ion concentration at the inlet of the cobalt removal process, thereby realizing the online accurate estimation of the copper ion concentration at the inlet of the cobalt removal process.
The model for estimating the concentration of the copper ions at the inlet of the cobalt removal process based on the associated process information is used as a direct downstream process of the copper removal process, the concentration of the copper ions at the inlet of the cobalt removal process mainly depends on the actual effect of the copper removal process, and the ORP can well reflect the change of the concentration of the copper ions at the inlet of the cobalt removal process. Therefore, the invention combines the information of the two processes to realize the online estimation of the copper ion concentration in the cobalt removal process, and the structural block diagram is shown in FIG. 7.
Therefore, the method and the device solve the problems that the concentration of the copper ions at the inlet of the cobalt removal process fluctuates greatly and online detection is difficult to carry out to bring difficulty to production optimization control of the cobalt removal process, can reflect the change trend of the concentration of the copper ions at the inlet of the cobalt removal process in real time, and provide guidance for optimization control of the cobalt removal process. The method of the invention was verified by selecting data of the purification process of a certain zinc smelter in Hunan, the results are shown in FIG. 8.
Referring to fig. 9, the system for estimating the concentration of copper ions at the inlet of the wet zinc smelting purification cobalt removal process provided by the embodiment of the invention comprises:
the device comprises a memory 10, a processor 20 and a computer program stored on the memory 10 and capable of running on the processor 20, wherein the processor 20 implements the steps of the method for estimating the concentration of copper ions at the inlet of the hydrometallurgical cobalt purification process when executing the computer program.
The specific working process and working principle of the system for estimating the concentration of copper ions at the inlet of the wet zinc smelting purification and cobalt removal process in this embodiment can refer to the working process and working principle of the method for estimating the concentration of copper ions at the inlet of the wet zinc smelting purification and cobalt removal process in this embodiment.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A method for estimating the concentration of copper ions at an inlet of a zinc hydrometallurgy cobalt-removing process is characterized by comprising the following steps:
according to the dynamics principle and the material balance principle of the copper removal process, a first model is established, wherein the first model is a relational model of the oxidation-reduction potential of the copper removal process and the concentration of copper ions in the copper removal liquid, and the establishment of the first model according to the dynamics principle and the material balance principle of the copper removal process comprises the following steps:
obtaining the relation between the activation energy and the potential based on the electrode reaction kinetics principle;
obtaining a chemical reaction constant based on an electrochemical reaction principle according to the relation between the activation energy and the potential;
obtaining a reaction rate based on a first-order chemical reaction in the copper removal process according to the chemical reaction constant and the concentration of copper ions at the outlet of the copper removal reactor;
establishing a first model based on a material balance principle according to a reaction rate, wherein the first model specifically comprises the following steps:
wherein the content of the first and second substances,
C1(t) represents the copper ion concentration of the solution after copper removal at time t,
represents the copper ion concentration at the inlet of the copper removal reactor, Q represents the flow rate of the zinc sulfate solution in the copper removal process, V represents the volume of the copper removal reactor, t0Represents the initial time, AβRepresents a pre-exponential factor, EeRepresents the standard activation energy parameter, alpha represents the proportionality coefficient, F represents the Faraday constant, eorpRepresents a potential value, eeqRepresents the standard equilibrium potential of copper ions, R represents the gas constant, and T represents the Kelvin temperature;
based on the first model, establishing a second model according to the residence time distribution relation, wherein the second model is a relation model of the copper ion concentration of the copper-removed liquid and the copper ion concentration of the copper-removal overflow, and the establishing of the second model according to the residence time distribution relation based on the first model comprises the following steps:
acquiring the probability that the fluid element entering the copper removal overflow groove in the previous n hours is still left in the overflow groove at the time t;
based on the first model, according to the probability, establishing a second model, wherein the second model specifically comprises:
wherein C is2(t) represents the copper ion concentration of the copper removal overflow at time t, C1(T-i delta T) represents the copper ion concentration of the copper-removed liquid in a time period delta T from the current time T to the previous i hours, i represents the ith hour time period in the previous n hour time periods, V2Representing the volume of the copper removal overflow tank, wherein DeltaT represents the time length of each short time segment, and Q (T-i DeltaT) represents the time period delta from the current time T to the previous time i hoursT, the flow rate of the copper-removed liquid, E (i Delta T) represents the probability of the residence time of Delta T from the current time T to the previous time by i hours;
establishing a KPLS model of a cobalt removal process, wherein the establishing of the KPLS model of the cobalt removal process comprises the following steps:
selecting the blanking amount of a zinc powder bin, the flow rate of a cobalt removal inlet solution, the additive amount of waste acid, the additive amount of arsenic salt and the oxidation-reduction potential of a cobalt removal reactor in the cobalt removal process to construct an input matrix of a KPLS (kernel nearest neighbor) model, and selecting the concentration variable of copper ions of copper removal overflow to construct an output matrix;
normalizing the input matrix and the output matrix;
introducing a Gaussian kernel function to map the input matrix to a high-dimensional feature space, obtaining a feature space matrix of the input matrix, and performing centralization processing on the feature space matrix;
establishing a KPLS model based on the feature space matrix after the centralization processing, wherein the KPLS model specifically comprises the following steps:
wherein K0Representing the feature space matrix after centralization, Y representing the output matrix, T and U respectively representing K0And a scoring matrix of Y, P and Q each representing K0And Y, E and F each represent K0And a residual matrix of Y, wherein A is the number of hidden variables reserved in the model, B and R are a regression coefficient matrix and a residual matrix, respectively
T＝[t1,t2,...tA]，U＝[u1,u2,...uA]，P＝[p1,p2,...pA]，Q＝[q1,q2,...qA]；
And establishing a cobalt removal process inlet copper ion concentration estimation model based on the first model, the second model and the KPLS model, and obtaining a cobalt removal process inlet copper ion concentration estimation value based on the cobalt removal process inlet copper ion concentration estimation model.
2. The method for estimating the concentration of copper ions at the inlet of the zinc hydrometallurgy purification cobalt removal process according to claim 1, wherein the establishing of the estimation model of the concentration of copper ions at the inlet of the cobalt removal process based on the first model, the second model and the KPLS model comprises:
obtaining a model coordination factor through fuzzy reasoning according to the online detection data of the copper removal process, wherein the obtaining of the model coordination factor through the online detection data of the copper removal process comprises the following steps:
selecting the variable quantity of the zinc powder as a first input variable, the variable quantity of the potential as a second input variable and the model coordination factor as an output variable;
determining a fuzzy set of the first input variable, the second input variable and the output variable according to a membership function, wherein the determining of the fuzzy set of the first input variable, the second input variable and the output variable according to the membership function specifically comprises:
determining a first fuzzy set of a first input variable, a second input variable and an output variable according to the Z-type membership function;
determining a first input variable, a second input variable and a second fuzzy set, a third fuzzy set and a fourth fuzzy set of output variables according to the bell-type membership function;
determining a fifth fuzzy set of the first input variable, the second input variable and the output variable according to the S-type membership function;
obtaining a model coordination factor by adopting a gravity center method according to the fuzzy set of the first input variable, the second input variable and the output variable based on a self-defined model coordination factor fuzzy rule;
establishing a cobalt removal process inlet copper ion concentration estimation model according to the first model, the second model, the KPLS model and the model coordination factor, wherein the cobalt removal process inlet copper ion concentration estimation model is specifically the copper ion concentration estimation model;
Ccombine＝μC2+(1-μ)Y，
wherein the content of the first and second substances,Ccombineestimation model of copper ion concentration at inlet for cobalt removal process, C2The concentration of copper ions in the copper removal overflow calculated according to the second model, Y is the concentration of copper ions in the copper removal overflow calculated according to the KPLS model, and mu is a model coordination factor.
3. A system for estimating the concentration of copper ions at the inlet of a zinc hydrometallurgy purification cobalt removal process, which comprises:
memory (10), processor (20) and computer program stored on the memory (10) and executable on the processor (20), characterized in that the steps of the method according to any of the preceding claims 1 to 2 are implemented when the computer program is executed by the processor (20).
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