阅读说明：本技术 一种沉铁过程出口离子预测方法及其系统 (Method and system for predicting outlet ions in iron precipitation process ) 是由 陈宁 万晶莹 戴佳阳 桂卫华 阳春华 陈嘉瑶 袁小锋 郭宇骞 于 2019-08-22 设计创作，主要内容包括：本发明公开了一种沉铁过程出口离子预测方法及其系统,通过获取同时包含溶解氧浓度的过程数据及其对应的溶解氧浓度数据的有标签样本集和仅包含溶解氧浓度的过程数据的无标签样本集；构建溶解氧浓度动态预测模型；并将溶解氧浓度动态预测模型与机理分析相结合,建立沉铁过程出口离子预测模型；使用粒子群算法和优化目标Ω求解出所述预测模型中的最优解,根据所述最优解对应的离子浓度来调节所述反应器的入口氧气浓度；相比起现有技术而言,使沉铁过程出口离子预测模型达到整体最优的同时保证预测值与实际值趋势相同,提高了预测的出口离子浓度的可信度和准确性。(The invention discloses a method and a system for predicting outlet ions in an iron precipitation process, wherein a labeled sample set which simultaneously contains process data of dissolved oxygen concentration and corresponding dissolved oxygen concentration data and a non-labeled sample set which only contains process data of dissolved oxygen concentration are obtained; constructing a dynamic prediction model of the dissolved oxygen concentration; combining a dynamic prediction model of the dissolved oxygen concentration with mechanism analysis to establish an outlet ion prediction model in the iron precipitation process; solving an optimal solution in the prediction model by using a particle swarm algorithm and an optimization target omega, and adjusting the inlet oxygen concentration of the reactor according to the ion concentration corresponding to the optimal solution; compared with the prior art, the prediction model of the outlet ions in the iron precipitation process achieves the overall optimization, meanwhile, the predicted value and the actual value have the same trend, and the reliability and the accuracy of the predicted outlet ion concentration are improved.)

1. A prediction method for iron precipitation process outlet ions is characterized by comprising the following steps:

acquiring process data containing dissolved oxygen concentration obtained by actual test and a labeled sample set corresponding to the dissolved oxygen concentration data in a single iron precipitation reactor, and constructing a non-labeled sample set only containing the process data of the dissolved oxygen concentration;

analyzing factors influencing an oxygen dissolving process in a goethite method iron precipitation process, and constructing a dissolved oxygen concentration variation prediction model by using the process data as first input data and using variation of the dissolved oxygen concentration between the previous sampling time and the next sampling time as first output data by using the labeled sample set and the unlabeled sample set;

according to the principle of dissolved oxygen concentration conservation at the previous and later sampling moments, a dissolved oxygen concentration dynamic prediction model which takes the dissolved oxygen concentration variation and the dissolved oxygen concentration as second input data and takes the dissolved oxygen concentration as second output data is constructed through the dissolved oxygen concentration variation prediction model;

on the basis of a single iron precipitation reactor, combining a dissolved oxygen concentration dynamic prediction model with mechanism analysis, establishing an iron precipitation process outlet ion prediction model taking the concentration of ferrous ions in a reactor inlet solution, the concentration of ferric ions in a reactor inlet solution, the concentration of hydrogen ions in a reactor inlet solution, the mass of zinc oxide added, the density of zinc oxide particles and the radius of the zinc oxide particles as third input data, and taking the concentration of ferrous ions in a reactor outlet solution, the concentration of ferric ions in a reactor outlet solution, the concentration of hydrogen ions in a reactor outlet solution and the concentration of oxygen in a reactor outlet solution as third output data;

acquiring third input data in the reactor to be predicted, inputting the third input data into the prediction model of the outlet ions of the iron precipitation process, and solving the optimal solution of the prediction model of the outlet ions of the iron precipitation process by using a particle swarm algorithm and an optimization target omega;

and adjusting the inlet oxygen concentration of the reactor according to the concentration of ferric ions in the reactor outlet solution, the concentration of hydrogen ions in the reactor outlet solution and the concentration of oxygen in the reactor outlet solution corresponding to the optimal solution.

2. The method of claim 1, wherein the process data comprises ferrous ion concentration in the reactor solution, ferric ion concentration in the reactor solution, copper ion concentration in the reactor solution, zinc ion concentration in the reactor solution, flow rate of the solution in the reactor, mass of iron slag generated in the reactor, mass of zinc oxide added, and oxygen flow rate into the reactor.

3. The method for predicting the outlet ions in the iron precipitation process according to claim 1, wherein the constructing of the dynamic prediction model of the dissolved oxygen concentration specifically comprises:

constructing a preliminary dissolved oxygen concentration variation prediction model between the previous sampling time and the next sampling time by using an instant learning algorithm of semi-supervised weighted probability partial minimum regression, and determining model parameters of the preliminary dissolved oxygen concentration variation prediction model;

acquiring query samples, selecting labeled samples and unlabeled samples from the labeled sample set and the unlabeled sample set to form a similar sample set of the query samples, and calculating the weight of each labeled likelihood sample and the weight of each unlabeled likelihood sample in the likelihood sample set by using first input data in the labeled likelihood samples and the unlabeled likelihood samples respectively;

using the weight of each labeled likelihood sample and the weight of each unlabeled likelihood sample to construct a weighted log-likelihood function containing the labeled likelihood sample, the unlabeled likelihood sample and the corresponding hidden variable;

solving an optimized model parameter by using the weighted log-likelihood function, and estimating the hidden variable posterior distribution of the model parameter by using the optimized model parameter;

substituting the optimized model parameters and the posterior distribution of the hidden variables of the model parameters into the preliminary prediction model of the dissolved oxygen concentration variation to obtain the prediction model of the dissolved oxygen concentration variation;

and then obtaining a dynamic prediction model of the dissolved oxygen concentration according to the conservation relation of the dissolved oxygen concentration change between the previous sampling time and the next sampling time.

4. The method for predicting outlet ions in an iron precipitation process according to claim 3, wherein the preliminary prediction model for the amount of change in dissolved oxygen concentration is:

x＝PH^s+QH^b+m_x+e_x

y＝CH^s+m_y+e_y

wherein, a hidden variable H is set^sRepresenting the relationship between the first input data and the first output data, while the hidden variable H^bRepresenting a correlation between the first input data; first input data x ∈ R^DAnd the first output data y ∈ R^MThe first input data and the first output data are respectively composed of hidden variablesGenerating through linear transformation;and

5. The method of claim 4, wherein calculating the weight of each labeled likelihood sample and the weight of each unlabeled likelihood sample in the set of likelihood samples comprises:

respectively calculating first distance data between the query sample and the labeled likelihood sample and second distance data between the query sample and the unlabeled likelihood sample by using first input data in the labeled likelihood sample and the unlabeled likelihood sample through a sample distance calculation formula;

wherein, the sample distance calculation formula is as follows:

wherein d is_r,sFor the distance calculation formula of the labeled sample and the query sample, d_r,tCalculating a formula for the distance between the unlabeled sample and the query sample; delta_sIs the inverse of the diagonal matrix, Δ, formed by the variances of the first input data in the labeled samples_tAn inverse matrix of a diagonal matrix composed of variances of the first input data in the unlabeled samples; x is the number of_tFor the first input data in unlabeled exemplars, x_qTo query a sample, x_sIs the first input data in the labeled exemplar, s represents the labeled exemplar, t represents the unlabeled exemplar, L₁And L₂Respectively representing the number of labeled samples and the number of unlabeled samples, wherein T represents a transposed symbol;

respectively calculating the weights of the labeled likelihood samples and the unlabeled likelihood samples by using the first distance data and the second distance data and through weight calculation formulas of the labeled samples and the unlabeled samples;

the weight calculation formula is as follows:

where i denotes labeled likelihood samples, j denotes unlabeled likelihood samples, ω_iWeight, ω, representing labeled likelihood samples_jRepresenting weights of unlabeled likelihood samples, σ being a distance parameter, d_r,iRepresenting first distance data, d_r,jRepresenting second distance data, n₁And n₂Representing the number of labeled likelihood samples and the number of unlabeled likelihood samples, respectively.

6. The method of claim 5, wherein the weighted log-likelihood function J is:

wherein J is represented as a weighted log-likelihood function; p represents a probability function; x is the number of_iInput data representing labeled likelihood samples; y is_iFirst output data of the labeled likelihood samples; x is the number of_jFirst input data representing unlabeled likelihood samples; h_i ^s,H_i ^bRespectively representing hidden variables in the labeled likelihood samples;

7. The method of claim 6, wherein solving the optimized model parameters using the weighted log-likelihood function is:

where E represents the expectation function, M represents the first output data dimension, Tr represents the traces of the matrix, and D represents the first input data dimension.

8. The method of claim 7, wherein the hidden variable posterior distribution is estimated as:

wherein the content of the first and second substances,expressed as the mean of the gaussian distribution satisfied by the posterior distribution of the hidden variables,

9. The method for predicting the outlet ions in the iron precipitation process according to claim 8, wherein the output submodel of the dissolved oxygen concentration variation prediction model is as follows:

wherein the content of the first and second substances,

10. The method of claim 9, wherein the prediction model of the outlet ions from the iron precipitation process is:

wherein the content of the first and second substances,

11. The method of claim 10, wherein the optimization objective Ω is

Wherein the optimization target J₁To obtain an optimization objective for the predicted value with the smallest error with the query sample, optimization objective J₂To obtain the optimization objective of the predicted value closest to the query sample variation trend, y_iActual third output data representing the ith training sample,third output data representing a prediction of an ith training sample,

12. A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 11 are performed when the computer program is executed by the processor.

Technical Field

The invention belongs to the technical field of wet zinc smelting and iron precipitation process control, and particularly relates to a prediction method and a prediction system for outlet ions in an iron precipitation process.

Background

Zinc is an important metal that can be used in various fields. The zinc smelting method mainly comprises the working procedures of ore grinding, leaching, purifying, electrolyzing and the like. The zinc sulfate solution in the method can be electrolyzed to obtain a zinc simple substance after being purified and purified. Currently, goethite is commonly used to remove the major impurity iron ions in zinc sulfate solutions. The main production equipment for the iron precipitation process is a Continuous Stirred Tank Reactor (CSTR), however, a single reactor cannot directly reduce the excess iron ions in the leachate to the process requirement range, and therefore iron removal is required in four reactors from high to low in cascade. The concentration of the ion at the outlet of the previous reactor is the concentration of the ion at the inlet of the next reactor, and the concentration of the iron ion at the outlet of each reactor needs to be reduced in turn according to the requirements, so that the iron ion content of the zinc sulfate solution is reduced to the range of the technological index requirements after the zinc sulfate solution leaves the last reactor. In order to achieve the purpose, the oxygen and the calcine added in each reactor need to be respectively adjusted according to the ion concentration at the inlet of each reactor (namely, the ion concentration at the outlet of the previous reactor), however, in the actual iron deposition process, the tightness of the reactor and the limitation of a detection device enable the concentration of each ion in the solution at the outlet of the reactor to be obtained only through manual periodic sampling detection. Resulting in a large hysteresis in the adjustment of the operating parameters (oxygen and calcine) and a reduction in the iron precipitation efficiency. Therefore, the method establishes an iron precipitation process model, predicts the concentration of ions at the outlet of the reactor, and has great significance for adjusting the blindness of the operating parameters and improving the iron precipitation efficiency.

Reasonably controlling Fe in the actual iron precipitation process²⁺The oxidation rate of (a) is the oxidation reaction rate is the key to achieving efficient iron removal. Fe²⁺Too fast an oxidation rate of (1) will result in Fe³⁺The content of (a) is too high, so that iron hydroxide colloid is generated, and the normal operation of hydrolysis reaction is influenced; the oxidation rate is too slow, so that the content of liquid iron ions after iron precipitation exceeds the standard, and the required iron removal effect cannot be achieved. While the oxidation reaction rate is controlled by the catalyst Cu²⁺The influence of the content is, in addition, controlled primarily by the flow of oxygen into the reactor. Therefore, obtaining the dissolved oxygen concentration is an important part of the mechanism modeling of the iron precipitation process. However, in the actual production process, due to high detection difficulty and the like, the dissolved oxygen concentration is not detected when the iron precipitation solution is subjected to offline sampling detection. And the oxygen solubility is influenced by various factors, and the mechanism model is difficult to consider the influence factors and simultaneously consider the computability and the accuracy. Because the concentration of dissolved oxygen is difficult to detect in the existing prediction model of the concentration of the outlet ions in the iron precipitation process, the accuracy of the predicted concentration of the outlet ions is low, so that the control behavior of adjusting the oxygen flow according to the predicted outlet ions cannot realize reasonable control of Fe²⁺The oxidation rate of the iron-removing catalyst can achieve the effect of efficiently removing iron.

Therefore, the existing prediction model for the concentration of the outlet ions in the iron precipitation process is difficult to detect the concentration of dissolved oxygen, so that the accuracy of the predicted concentration of the outlet ions is not high, and the technical problem to be solved by the technical personnel in the field is urgently needed.

Disclosure of Invention

The invention provides a prediction method of outlet ions in an iron precipitation process, which is used for solving the technical problem that the accuracy of the predicted outlet ion concentration is low because the concentration of dissolved oxygen is difficult to detect in an existing prediction model of the outlet ion concentration in the iron precipitation process.

In order to solve the technical problems, the technical scheme provided by the invention is as follows:

a prediction method for iron precipitation process outlet ions comprises the following steps:

acquiring process data containing dissolved oxygen concentration obtained by actual test and a labeled sample set corresponding to the dissolved oxygen concentration data in a single iron precipitation reactor, and constructing a non-labeled sample set only containing the process data of the dissolved oxygen concentration;

analyzing factors influencing an oxygen dissolving process in a goethite method iron precipitation process, and constructing a dissolved oxygen concentration variation prediction model by using the process data as first input data and using variation of the dissolved oxygen concentration between the previous sampling time and the next sampling time as first output data by using the labeled sample set and the unlabeled sample set;

according to the principle of dissolved oxygen concentration conservation at the previous and later sampling moments, a dissolved oxygen concentration dynamic prediction model which takes the dissolved oxygen concentration variation and the dissolved oxygen concentration as second input data and takes the dissolved oxygen concentration as second output data is constructed through the dissolved oxygen concentration variation prediction model;

on the basis of a single iron precipitation reactor, combining a dissolved oxygen concentration dynamic prediction model with mechanism analysis, establishing an iron precipitation process outlet ion prediction model taking the concentration of ferrous ions in a reactor inlet solution, the concentration of ferric ions in a reactor inlet solution, the concentration of hydrogen ions in a reactor inlet solution, the mass of zinc oxide added, the density of zinc oxide particles and the radius of the zinc oxide particles as third input data, and taking the concentration of ferrous ions in a reactor outlet solution, the concentration of ferric ions in a reactor outlet solution, the concentration of hydrogen ions in a reactor outlet solution and the concentration of oxygen in a reactor outlet solution as third output data;

acquiring third input data in the reactor to be predicted, inputting the third input data into the prediction model of the outlet ions of the iron precipitation process, and solving the optimal solution of the prediction model of the outlet ions of the iron precipitation process by using a particle swarm algorithm and an optimization target omega;

and adjusting the inlet oxygen concentration of the reactor according to the concentration of ferric ions in the reactor outlet solution, the concentration of hydrogen ions in the reactor outlet solution and the concentration of oxygen in the reactor outlet solution corresponding to the optimal solution.

Preferably, the process data includes the concentration of ferrous ions in the reactor solution, the concentration of ferric ions in the reactor solution, the concentration of copper ions in the reactor solution, the concentration of zinc ions in the reactor solution, the flow rate of the solution in the reactor, the mass of iron slag generated in the reactor, the mass of zinc oxide added, and the flow rate of oxygen introduced into the reactor.

Preferably, the constructing of the dynamic prediction model of dissolved oxygen concentration specifically includes:

constructing a preliminary dissolved oxygen concentration variation prediction model between the previous sampling time and the next sampling time by using an instant learning algorithm of semi-supervised weighted probability partial minimum regression, and determining model parameters of the preliminary dissolved oxygen concentration variation prediction model;

acquiring query samples, selecting labeled samples and unlabeled samples from the labeled sample set and the unlabeled sample set to form a similar sample set of the query samples, and calculating the weight of each labeled likelihood sample and the weight of each unlabeled likelihood sample in the likelihood sample set by using input data in the labeled likelihood samples and the unlabeled likelihood samples respectively;

using the weight of each labeled likelihood sample and the weight of each unlabeled likelihood sample to construct a weighted log-likelihood function containing the labeled likelihood sample, the unlabeled likelihood sample and the corresponding hidden variable;

solving an optimized model parameter by using the weighted log-likelihood function, and estimating the hidden variable posterior distribution of the model parameter by using the optimized model parameter;

substituting the optimized model parameters and the posterior distribution of the hidden variables of the model parameters into the preliminary prediction model of the dissolved oxygen concentration variation to obtain the prediction model of the dissolved oxygen concentration variation;

and then obtaining a dynamic prediction model of the dissolved oxygen concentration according to the conservation relation of the dissolved oxygen concentration change between the previous sampling time and the next sampling time.

Preferably, the preliminary prediction model of the dissolved oxygen concentration variation is as follows:

x＝PH^s+QH^b+m_x+e_x

y＝CH^s+m_y+e_y

wherein, a hidden variable H is set^sRepresenting the relationship between the first input data and the first output data, while the hidden variable H^bRepresenting a correlation between the first input data; first input data x ∈ R^DAnd the first output data y ∈ R^MThe first input data and the first output data are respectively composed of hidden variables

Generating through linear transformation;

and

is a load matrix for the first input data,

a load matrix being the first output data; m is_xAnd m_yMean or offset of the first input data and the first output data, respectively; e.g. of the type_xAnd e_yNoise terms for the first input data and the first output data, respectively, which are both subject to an isotropic Gaussian distribution e_x～N(0,ε_xI) And e_y～N(0,ε_yI) (ii) a Hidden variable

The prior distribution obeys a Gaussian distribution H^sN (0, I), similarly, hidden variables

Priori distributed compliance H^bN (0, I), the parameter set of the model is Θ ═ m_x,m_y,P,Q,C,ε_x,ε_y}，R^DRepresenting a D1 matrix of real numbers, R^MA matrix of real numbers representing M x 1,represents K_sA matrix of real numbers x 1, and,represents K_bX 1 matrix of real numbers, I denotes the identity matrix, ε_yRepresenting the noise variance, ε, of the first input data_xRepresenting the noise variance of the first output data.

Preferably, the calculating the weight of each labeled likelihood sample and the weight of each unlabeled likelihood sample in the likelihood sample set includes:

respectively calculating first distance data between the query sample and the labeled likelihood sample and second distance data between the query sample and the unlabeled likelihood sample by using the input data in the labeled likelihood sample and the unlabeled likelihood sample through a sample distance calculation formula;

wherein, the sample distance calculation formula is as follows:

wherein d is_r,sFor the distance calculation formula of the labeled sample and the query sample, d_r,tCalculating a formula for the distance between the unlabeled sample and the query sample; delta_sIs the inverse of the diagonal matrix, Δ, formed by the variances of the first input data in the labeled samples_tAn inverse matrix of a diagonal matrix composed of variances of the first input data in the unlabeled samples; x is the number of_tFor the first input data in unlabeled exemplars, x_qTo query a sample, x_sIs the first input data in the labeled exemplar, s represents the labeled exemplar, t represents the unlabeled exemplar, L₁And L₂Respectively, the number of labeled samples and the number of unlabeled samples, and T denotes a transposed symbol.

Respectively calculating the weights of the labeled likelihood samples and the unlabeled likelihood samples by using the first distance data and the second distance data and through weight calculation formulas of the labeled samples and the unlabeled samples;

the weight calculation formula is as follows:

where i denotes labeled likelihood samples, j denotes unlabeled likelihood samples, ω_iWeight, ω, representing labeled likelihood samples_jRepresenting weights of unlabeled likelihood samples, σ being a distance parameter, d_r,iRepresenting first distance data, d_r,jRepresenting second distance data, n₁And n₂Representing the number of labeled likelihood samples and the number of unlabeled likelihood samples, respectively.

Preferably, the weighted log-likelihood function J is:

wherein J is represented as a weighted log-likelihood function; p represents a probability function; x is the number of_iInput data representing labeled likelihood samples; y is_iFirst output data of the labeled likelihood samples; x is the number of_jFirst input data representing unlabeled likelihood samples; h_i ^s,H_i ^bRespectively representing hidden variables in the labeled likelihood samples;

respectively, representing hidden variables in the unlabeled likelihood samples.

Preferably, the solving of the optimized model parameters by using the weighted log-likelihood function is as follows:

where E represents the expectation function, M represents the first output data dimension, Tr represents the traces of the matrix, and D represents the first input data dimension.

Preferably, the posterior distribution of hidden variables is estimated as:

wherein the content of the first and second substances,

expressed as the mean of the gaussian distribution satisfied by the posterior distribution of the hidden variables,

expressed as the variance of the gaussian distribution satisfied by the posterior distribution of the hidden variables,

for unlabeled data hidden variables H_j ^sThe variance of (c).

Preferably, the output submodel of the dissolved oxygen concentration variation prediction model is:

wherein the content of the first and second substances,

to look up the predicted dissolved oxygen concentration change for the sample,

wherein C is a load matrix of the dissolved oxygen concentration variation,expressed as the mean of the gaussian distribution satisfied by the posterior distribution of the hidden variable.

Preferably, the prediction model of the outlet ions in the iron precipitation process is as follows:

wherein the content of the first and second substances,

an output sub-model of the dissolved oxygen concentration dynamic prediction model; t is the formula t, at represents the step size, V is the volume of the reactor, F is the flow rate of the solution in the reactor,respectively the concentration of Fe2+, the concentration of Fe3+, the concentration of H + in the solution at the inlet of the reactor,

respectively the ion concentration in the solution at the outlet of the reactorDegree, k₁,k₂,k₃Respectively is the reaction rate constant of oxidation, hydrolysis and neutralization reactions, alpha, beta and gamma are reaction stages of three reactions in which ferrous ions, hydrogen ions and dissolved oxygen participate in the iron precipitation process, m is the mass of the added neutralizer zinc oxide, rho is the particle density of the zinc oxide, and R is the particle density of the zinc oxide_sIs the zinc oxide particle radius.

Preferably, the optimization target Ω is

Wherein the optimization target J₁To obtain an optimization objective for the predicted value with the smallest error with the query sample, optimization objective J₂To obtain the optimization objective of the predicted value closest to the query sample variation trend, y_iActual third output data representing the ith training sample,

third output data representing a prediction of an ith training sample,is the mean of the i-th training sample, y_i-1The actual third output data of the i-1 st training sample,

third output data representing a prediction of the (i-1) th training sample,then it is the average of the i-1 st training samples and N is the number of test samples.

A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the computer program.

Compared with the prior art, the invention has the beneficial effects that:

according to the method and the system for predicting the outlet ions in the iron precipitation process, the prediction model of the outlet ions in the iron precipitation process is obtained by combining the dynamic prediction model of the dissolved oxygen concentration with the mechanism analysis. And finally, adding the trend consistency index into an optimization target when the integral identification optimization is carried out on the prediction model of the outlet ions in the iron precipitation process. The prediction model of the outlet ions of the iron precipitation process achieves the overall optimization, meanwhile, the predicted value and the actual value have the same trend, and the reliability of the model is improved. Compared with the prior art, the outlet ion concentration predicted by using the prediction model of the outlet ions in the iron precipitation process is higher in accuracy, the inlet oxygen concentration of the reactor is adjusted according to the ferric ion concentration corresponding to the optimal solution, the hydrogen ion concentration in the outlet solution of the reactor and the oxygen concentration in the outlet solution of the reactor, and Fe can be more reasonably controlled²⁺The oxidation rate of the iron-removing catalyst can achieve the effect of efficiently removing iron.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a flow chart of a method for predicting an outlet ion in an iron precipitation process according to the present invention;

FIG. 2 is a CSTR system of a single reactor used in the mechanistic analysis of the iron precipitation system of the present invention;

FIG. 3 is a comparison graph of predicted values and actual values of the integrated model according to the present invention.

Detailed Description

In order to facilitate understanding of the invention, the invention will be described more fully and in detail with reference to the accompanying drawings and preferred embodiments, but the scope of the invention is not limited to the specific embodiments below.

Unless otherwise defined, all terms of art used hereinafter have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present invention.

Unless otherwise specifically stated, various raw materials, reagents, instruments, equipment and the like used in the present invention are commercially available or can be prepared by existing methods.