CN112685901B - Calorimeter calculation method - Google Patents
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Abstract
The invention relates to a calorimeter calculation method, in particular to a calorimeter algorithm capable of calculating a balance final value in a dynamic transition zone. The method comprises four execution stages, namely a basic data collection stage, a system characteristic value analysis stage, a curve shape screening stage and a balance final value calculation stage. According to the invention, by introducing an immediate iterative numerical calculation algorithm to the acquired data to process, summarizing the data rule of the dynamic interval of the measurement process and deducing the running trend of the system, the measurement result can be obtained in the dynamic area period before reaching the steady state, so that the measurement does not need to rely on the establishment of the steady state of the whole heat balance of the system, the calorimetric measurement time can be obviously shortened, and the time efficiency of calorimetric measurement is greatly improved.
Description
Technical Field
The invention belongs to a calorimetric technique, and particularly relates to a calorimetric algorithm capable of calculating a balance final value in a dynamic transition zone.
Background
The existing calorimetric technology generally relies on temperature-dependent quantities for thermal power characterization, and then calculates the mass of the nuclide from the nuclide characteristic power values. The characterization of the thermal power depends on the establishment of a thermal equilibrium state, and a sample to be measured forms a stable temperature gradient in the calorimetric element to effectively output a thermal power related signal value. In the prior art, the time for establishing heat balance is long, and the time efficiency of calorimeter measurement is low.
The proper physical improvement can shorten the calorimetric measurement time to a certain extent, and is more typical, such as fine structure design, introduction of a preheating system, adoption of a servo measurement mode and the like, but the physical modification forming, namely solidification, has limited adaptability, does not change the essence of measurement dependent balance establishment, and has limited effect.
Disclosure of Invention
The invention aims to provide a calorimeter calculation method based on the existing calorimeter technical physical design, and introduces a prediction algorithm design, calculates a predicted equilibrium state final value by utilizing transition state interval data of which the calorimeter measurement has not established heat balance, effectively reduces the necessary input time for obtaining effective data by the calorimeter measurement, and improves the time efficiency of calorimeter measurement.
The technical scheme of the invention is as follows: a method of calorimeter calculation comprising the steps of:
(1) Collecting basic data, forming a minimum data set which can be operated by a calculation method, wherein the basic data is the change relation of a thermal power correlation signal value of the entity calorimetric equipment per se along with time;
(2) Continuously collecting new basic data, entering a basic data set, performing instant numerical calculation and system characteristic value analysis processing by using the formed new basic data set, and generating a characteristic time constantA balancing criterion;
(3) Characteristic time constant for generationCarrying out algorithm processing on the data set of the database (C), generating a stability criterion, and returning to the step (1) to change parameter setting if the data set is an irregular curve in the form of the judging curve, and carrying out basic data collection again; if the curve is the normal curve, the step (4) is carried out;
(4) And (3) carrying out calculation and determination of a balance curve and a final value to generate a balance prediction final value A and a measurement uncertainty dA.
According to one embodiment, the thermal power-related signal of the bulk calorimeter itself described in step (1) is an observable signal capable of responding to a temperature change, as described above in the calorimeter method.
Further, the thermal power related signal is a thermoelectric signal.
According to one embodiment, the calorimeter method as described above, the collection length of the basic data described in step (1) satisfies the algorithm calculation requirement t E +t S Wherein t is E For the offset time, representing a data point set to be discarded before the algorithm acts, and used for removing irregular data possibly existing in an initial state; t is t S Is the least significant underlying data set;
according to one embodiment, the analysis process described in step (2) is performed over time by a calorimetric method as described above.
Further, the step (2) includes various data preprocessing methods such as outlier rejection, data smoothing, noise separation, etc. which can be used to improve or change the curve form so as to better perform the subsequent analysis process.
Further, the system characteristic value analysis in the step (2) adopts nonlinear iterative fitting of a least square method.
Further, the core algorithm of system characteristic value analysis in the step (2) is a multi-exponential term prediction algorithm.
Further, the eigenvalue analysis in the step (2) is implemented in four steps: 1) Determining parameters required by nonlinear iterative fitting entry initial value calculation; 2) Generating an entry index component and an entry log component using the parameter calculation determined in step 1); 3) Reasonably combining the exponential component and the logarithmic component to generate an initial value of the entry; 4) And 3) based on the initial value of the inlet determined in the step 3), performing nonlinear iterative fitting to generate a transient system characteristic value.
According to one embodiment, the balance criterion in step (2) is t, as described above for the calorimetric method i Time of day predictionFinal value oft i-1 Time prediction end value +.>t i-1 Fitting time to the next time value +.>t i Time measured signal valuet i Time measured signal standard deviation->Characteristic time constant->And->The associated gain factor k is determined.
Further, the following relationship is adopted as a balance criterion for whether final value prediction can be performed:
wherein:
representing t i Predicting signal end value at time, mV
Representing t i-1 Predicting signal end value at time, mV
Representing t i-1 Fitting the next time signal value at the moment, mV
Representing t i Measured signal value at moment, mV
Representing t i Time actual measurement signal standard deviation, mV
The characteristic time constant is represented by a characteristic time constant,
k representsThe gain factor associated with the gain factor is,
t C total test time, min.
According to one embodiment, the stability criterion in step (3) is calculated in real time using a plurality of consecutive times, as described aboveThe degree of value dispersion is determined.
Further, if the stability criterion is poor (large in value), an irregular curve is considered.
According to one embodiment, the measurement uncertainty in step (4) can be characterized by the following formula:
wherein:
D i representing t i The signal value is actually measured at the moment,
f(t i ) Representing t i The signal values are fitted at the moment in time,
n represents the number of fitting data points,
m represents the number of fitting parameters.
The beneficial effects of the invention are as follows: on the basis of the existing steady-state calorimetric technique, the prediction algorithm of the invention introduces an immediate iterative numerical calculation algorithm to the acquired data to process, generalizes the data rule of the dynamic interval of the measurement process, and deduces the running trend of the system, thereby obtaining the measurement result in the dynamic region period before reaching the steady state, ensuring that the measurement does not depend on the establishment of the steady state of the whole heat balance of the system, obviously shortening the calorimetric measurement time and greatly improving the time efficiency of calorimetric measurement. The method does not involve any entity transformation, utilizes the existing data acquisition system, adopts the modes of data backup, data partition and algorithm design in the original data processing terminal to process data, does not influence the data acquisition and processing of the original steady-state balance measurement, ensures that the transformed calorimetric system has the steady-state measurement and balance prediction functions, and can widen the application space.
Drawings
FIG. 1 is a diagram illustrating the intervals of action of a balance prediction algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the operation logic of the balance prediction algorithm in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a calorimeter calculation method, which is a balance final value prediction algorithm and comprises the following four execution stages which are sequentially executed:
basic data collection phase
And the stage performs basic data acquisition and accumulation according to the relevant settings. The basic data is the time-dependent change relation of the thermal power associated signal value of each entity calorimetric equipment, and the stage of accumulation generates the minimum data set which can be operated by the prediction core algorithm.
(II) System characteristic value analysis stage
The data is continuously collected, the instant numerical value calculation and the system characteristic value analysis are carried out at the stage. The system continuously supplements and appends the newly acquired data into the basic data set, and immediately analyzes and processes the newly acquired data set to generate a characteristic time constantAnd (5) a balance criterion.
(III) Curve shape discrimination stage
And at this stage, discriminating the data set curve form. For the second stageThe data set is processed by an algorithm to generate a stability criterion, and the stability criterion is determined in the form of a curve, wherein the algorithm is a known technology and is not unique, and can be calculated by adopting the relative standard deviation/standard deviation within a certain time range, so as to represent ∈>Is a stable degree of (c). If the curve is an irregular curve, the relevant parameter setting of the basic data collection stage (I) is changed, and basic data collection is carried out again. The parameter changed is the offset time, that is, +.>Instability indicates that the curve does not reach the ideal predicted state, and the offset time t needs to be increased continuously E . If the curve is a conventional curve, the phase (IV))。
(IV) balance final value calculation stage
And in this stage, the balance curve and the final value are calculated and determined. And (3) immediately judging the feasibility of the balance prediction by the system after entering the execution stage (II), and carrying out the balance prediction once the balance criterion is met to generate a balance prediction final value A and a measurement uncertainty dA.
As a preferred embodiment, the thermal power related signal of the entity's thermo-mechanical device of stage (one) is an observable signal that is capable of responding to temperature changes.
Further, the thermal power related signal is a thermoelectric signal.
As a preferred embodiment, stage (one) the basic data collection length satisfies the algorithm calculation t E +t S Wherein t is E For the offset time, representing a data point set to be discarded before the algorithm acts, and used for removing irregular data possibly existing in an initial state; t is t S Is a custom minimum valid base data set.
As a preferred embodiment, the instant data parsing process described in stage (two) proceeds over time in a loop.
Further, the instant numerical computation may include various types of data preprocessing methods such as outlier rejection, data smoothing, noise separation, etc. that may be used to improve or change the curve to better perform the subsequent parsing process. These methods are well known and the specific method can be selected as desired.
Further, the eigenvalue analysis method is nonlinear iterative fitting by adopting a least square method.
Further, the eigenvalue analysis core algorithm is a multi-exponential term prediction algorithm.
Further, the eigenvalue analysis is realized in four steps: 1) Determining parameters required by nonlinear iterative fitting entry initial value calculation; 2) Generating an entry index component and an entry log component using the parameter calculation determined in step 1); 3) Reasonably combining the exponential component and the logarithmic component to generate an initial value of the entry; 4) And 3) based on the initial value of the inlet determined in the step 3), performing nonlinear iterative fitting to generate a transient system characteristic value.
Further, parameters required for calculating the initial value of the entry can be determined according to the maximum/minimum value of the thermoelectric signals in the collected data set and the corresponding time index value, and the index component and the log component sign item are determined according to the time index.
Further, the entry index component and entry log component parameters may be determined using respective fits.
Further, the entry-initial-value parameter may be generated from the entry-exponent component and the logarithmic component (including the corresponding sign term) through basic operations such as exponentiation, reciprocal, and the like.
As a preferred embodiment, the stage (two) balancing criteria content may be set specifically based on the physical calorimetric system characteristics. The balance criteria of the algorithm are not unique, and proper criteria can be selected according to the test conditions of the physical system. In general, factors such as fluctuation conditions of the predicted and measured data, deviation conditions of the predicted data and the existing measured data, input test time, stability of a system characteristic value and the like can be taken into consideration in a balance criterion.
Further, the balancing criterion may employ t i Time prediction final valuet i-1 Time prediction end value +.>t i-1 Fitting time to the next time value +.>t i Time measured signal value +.>(thermoelectric potential Signal), t i Standard deviation of time actual measurement signalCharacteristic time constant->And->The associated gain factor k is determined.
Further, the following relationship may be employed as a balance criterion for whether final value prediction is possible:
wherein:
—t i predicting signal end value at time, mV
Representing t i-1 Predicting signal end value at time, mV
—t i-1 Fitting the next time signal value at the moment, mV
—t i Measured signal value at moment, mV
—t i Time actual measurement signal standard deviation, mV
Characteristic time constant
k-andcorrelated gain coefficient
t C Total test time, min.
As a preferred embodiment, the stage (three) stability criteria may be set specifically based on the characteristics of the physical calorimetric system. The principle is the same as the balance criterion setting of the second stage.
Further, the stability criterion is calculated continuously and repeatedly in real timeThe degree of value dispersion (standard deviation characterization) is determined.
Further, the number of measurement points used for the stability criterion calculation may be set as desired within a reasonable range, for example 10 times.
Further, if the stability criterion is poorLarger discrete values), the non-conventional curve is identified, and the process performs adaptive parameter setting to reenter the phase (one).
Further, the poor stability criterion may be set toDiscrete value of value>10。
As a preferred embodiment, the uncertainty of the stage (four) fitting result can be characterized by the following formula:
wherein:
D i —t i the signal value is actually measured at the moment,
f(t i )—t i fitting signal values at time, i.e. t i Generating a group of determined parameters by real measurement data and nonlinear iterative fitting based on multiple exponential terms at moment, and then adding t to the obtained parameters i Substituting the numerical value obtained by the moment parameters of the multi-exponential term formula obtained by fitting,
n-the number of fitting data points,
m-number of fitting parameters.
Examples
As shown in fig. 1 and 2, the temperature-related signal is a thermoelectric voltage, for example, in a thermopile calorimeter. The acquisition of the balance final value is completed by the following phase sequence execution.
Basic data collection
Basic data acquisition setting: offset time t E =10 min, the least significant basis data set t S Data acquisition period t=1min.
Li Yongliang the thermal entity itself data system acquires and accumulates a system potential-time relationship data set, base data collection time t E +t S ＝30min。
(II) System characteristic value resolution
The instant characteristic time constant analysis is started immediately after the next data point (namely after the system runs for 30 min) is collected by the minimum basic data set, all data points in the effective data set at the moment are taken as data sources, and three indexes are usedFor calculating the model, a nonlinear iterative mode of a least square method is adopted to carry out characteristic time constant +.>To obtain the analysis time constant at the moment. Calculating the model multi-index number as the related quantity, non-definite number and practical number of the heat conduction critical path material types in the systemSystem-specific; constant term a [6] The predicted final value of the temperature difference sensitive signal is directly related to the test power; the method comprises the steps of carrying out a first treatment on the surface of the Index term coefficient a [i] Correlation coefficients for the differences between the test sample and the system environment; index item index->The heat capacity and the heat conductivity of the heat conduction material are related.
With the lapse of measurement time, continuously obtaining analysis time constant corresponding to the moment of acquiring new data points, and when the accumulated analysis time reaches 10 times, performing 10 times of continuous analysis time constant standard deviationAnd calculating and characterizing the discrete degree. If the standard deviation meets the design requirement (specifically, can be set according to the prediction accuracy requirement, 10 in this example), a deviation limit value is generated; the gain factor k is determined based on the length of time (dCT) from the point of time when the deviation limit is satisfied to the point of time when the analysis time constant standard deviation calculation is started. dCT characterizes the convergence speed of the algorithm. Determining whether balance judgment is feasible or not according to the following balance criterion calculation at the moment, and entering a stage (fourth) if the balance judgment is feasible; and if not, the curve shape discrimination calculation of the stage (III) is assisted.
Balance criterion:
wherein:
—t i predicting the final value of the potential at the moment, m
Representing t i-1 Predicting signal end value at time, mV
—t i-1 Fitting the potential value at the next moment in time, mV
—t i Measured potential value at moment, mV
—t i Time actual measurement signal standard deviation, mV
Characteristic time constant
k-andcorrelated gain coefficient
t C Total test time, min.
The uncertainty of the fitting result is characterized by the following formula:
wherein:
D i —t i measuring potential value, mV at the moment
f(t i )—t i Fitting potential values at the moment, mV
n-the number of fitting data points,
m-number of fitting parameters.
(III) Curve shape discrimination
Stage (II) standard deviation of time constant in 10 continuous analysesCharacterization of the stability of the characteristic time constant, if->Always at all times>10, the curve is irregular and the offset time t is redefined E To exclude invalid data point sets. In this example, the time t is gradually increased according to the data acquisition interval E Until the stability of the characteristic time constant meets the design requirement.
(IV) balance final value calculation
Adopting a three-exponential term form to carry out algorithm design, obtaining a characteristic time constant in the stage (II), generating a group of determined parameters after nonlinear iterative fitting is completed, and shifting the test time in the x direction for an infinite length to obtain a balance potential prediction final value E P ＝a [6] 。
The parameters in the above formula are the same as those in the second stage.
In order to test the deviation degree of the balance prediction final value and the actual measurement final value, two functions of balance prediction and steady-state measurement are started simultaneously when the test is implemented, and the required time of the steady-state measurement is taken as a reference standard to obtain the calorimetric measurement performance in two modes of the balance prediction and the steady-state measurement as follows:
note that: (1) The timing in the table represents the steady-state measurement timing criterion, i.e. the measurement is ended after the specified time is reached, in this example the timing time is 16 hours of the normal running time of the calorimeter.
(2) A represents a predicted equilibrium potential value calculated by a prediction mode, and E represents a measured equilibrium potential value measured by steady state measurement.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. Thus, if such modifications and application adaptations to the present invention fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and application adaptations as well.
The above embodiments are merely illustrative of the present invention, and the present invention may be embodied in other specific forms or with other specific forms without departing from the spirit or essential characteristics thereof. The described embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.
Claims (4)
1. A method of calorimeter calculation comprising the steps of:
(1) Basic data collection is carried out to form a minimum data set which can be operated by a calculation method, wherein the basic data is the change relation of the thermal power related signal value of the entity calorimetric equipment per se along with time, and the thermal power related signal of the entity calorimetric equipment per se is an observable signal capable of responding to temperature change; the collection length of the basic data meets the t required by algorithm calculation E +t S Wherein t is E For offset time, representing the set of data points to be discarded before the algorithm is applied to remove irregular data possibly existing in the initial state, t S Is the least significant underlying data set;
(2) Analyzing characteristic time constant and determining balance criterion, taking all data points in the data set as data sources and taking multiple indexesFor calculating the model, use is made ofThe nonlinear iterative fitting of the least square method is used for carrying out analytical calculation on a characteristic time constant tau, and the multi-exponential term of the calculation model is the related quantity of the heat conduction critical path material category in the system, and the constant term a [6] The index term coefficient a is the predicted final value of the temperature difference sensitive signal [i] To test the correlation coefficient of the sample and the system environment difference, the index term index τ i The heat capacity and the heat conductivity of the heat conduction material are related coefficients; the nonlinear iterative fitting of the least square method is realized in four steps: 1) Determining parameters required by nonlinear iterative fitting entry initial value calculation, determining the parameters required by the entry initial value calculation according to maximum/minimum values of observable signals in the collected data set and corresponding time index values, and judging index components and logarithmic component symbol items according to the time index values; 2) Generating an entry index component and an entry log component using the parameter calculation determined in step 1); 3) Combining the exponential component and the logarithmic component to generate an initial entry value; 4) Based on the initial value of the inlet determined in the step 3), nonlinear iterative fitting is carried out to generate a transient system characteristic value;
taking the fluctuation condition of the predicted and measured data, the deviation condition of the predicted data and the existing measured data, the input test time and the stability degree of the system characteristic value as the consideration range of the balance criterion;
(3) Determining a stability criterion by adopting the tau value discrete degree obtained by continuous and multiple instant calculation, and returning to the step (1) to change parameter setting and re-collecting basic data if the tau value discrete degree is an irregular curve in the form of the judging curve; if the curve is the normal curve, the step (4) is carried out;
(4) Performing calculation and determination of the balance curve and the final value to generate a balance prediction final value A and a measurement uncertainty dA,
after the nonlinear iterative fitting is completed, generating a group of determined parameters, and shifting the test time t to an infinite length to obtain a balance prediction final value A;
the measurement uncertainty is characterized by the following formula:
wherein:
D i representing t i The signal value is actually measured at the moment,
f(t i ) Representing t i The signal values are fitted at the moment in time,
n represents the number of fitting data points,
m represents the number of fitting parameters.
2. The calorimeter method of claim 1, wherein the thermal power-related signal is a thermoelectric signal.
3. The calorimeter method of claim 1, wherein the balance criterion in step (2) is t i Time prediction final valuet i-1 Time prediction end value +.>t i-1 Fitting time to the next time value +.>t i Time measured signal valuet i Time measured signal standard deviation->A characteristic time constant tau and a gain coefficient k related to tau are determined;
the following relationship is used as a balance criterion for whether final value prediction can be performed:
t C ≥k*τ
wherein:
representing t i The final value of the time instant predicted signal,
representing t i-1 The final value of the time instant predicted signal,
representing t i-1 Fitting the next time signal value from time to time,
representing t i The signal value is actually measured at the moment,
representing t i The standard deviation of the signal is measured at the moment,
τ represents a characteristic time constant of the device,
k denotes the gain factor associated with tau,
t C indicating the total test time.
4. The method of claim 1, wherein in step (3), if the τ value is more discrete than the set point, then determining an irregular curve.
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