This application claims priority from Chinese Application Serial Number 201811250468.0 filed Oct. 25, 2018, which is hereby incorporated herein by reference in its entirety.
The invention relates to a measurement method of concentration-dependent diffusion coefficients of binary solutions in the field of liquid mass transfer process.
Diffusion coefficient (D) is the important datum for the study of liquid mass transfer process, and its accurate measurement has important scientific significance and application value for physics, chemical industry, biology, medicine, environmental protection and other scientific fields. D value is generally concentration-dependent (D(C)), unfortunately, there is not a general theory to date that gives satisfactory D(C) description even in binary liquid diffusion processes, therefore, experiments are usually necessary in order to determine precise D(C) of different diffusion system required in mass transfer calculation.
Diaphragm cell[1] was utilized to measure concentration-dependent liquid diffusion coefficients in the aqueous solutions of HCl, authors retrieved information about the first and second derivative of the diffusion coefficient with respect to concentration, D(C) was given in the form of Taylor series. It is necessary for Diaphragm cell method to carry on a number of diffusion experiments with different concentrations. Holographic interferometry[2] was applied to acquire concentration-dependent liquid diffusion coefficients in the aqueous solutions of KCl, D(C) was given in the form of polynomial. Holographic interferometry method also requires a number of experiments and extremely strict experimental environment. Raman spectroscopy was used to measure liquid diffusion coefficients of binary systems of benzene/n-hexane, benzene/cyclohexane, benzene/acetone[3] and ethyl acetate/cyclohexane[4], concentration-dependent liquid diffusion coefficients were listed in tables and in the form of polynomial, respectively. Because Raman scattering is a weak effect, strong Stocks band associated with diffusion component must exist and an expensive Raman image spectrometer is necessary. Nuclear magnetic resonance was used to measure liquid diffusion coefficients of TEA/H2O[5] and cyclohexane/n-hexane/toluene[6], which required the target nucleus had non-vanishing nuclear magnetic moment, and expensive equipment. Besides the disadvantages mentioned above, those four methods are time-consuming processes and have a heavy workload.
To rapidly and precisely measure concentration-dependent diffusion coefficients of binary solutions from a single experiment and observe directly liquid diffusion processes, the present invention acquires diffusion image using an asymmetric liquid-core cylindrical lens (ALCL)[7-9], the concentration spatial and temporal profile of diffusion solution, Ce(z, t)s, can be deduced from the diffusion images. The algorithm of finite difference method (FDM)10] is applied to solve numerically Fick diffusion equation, the calculated diffusion concentration profiles, Cn(z, t)s, are compared with experimental concentration profiles to determine concentration-dependent diffusion coefficient D(C) in the invention.
One of positive result of the invention is that only a single diffusion image taken at suitable time (t0) is required to rapidly measure concentration-dependent liquid D(C), therefore, the invention can greatly shorten the experimental time required to measure concentration-dependent liquid D(C).
Another positive result of the invention is that the invention provides a new method for measuring concentration-dependent liquid D(C) precisely. The method calculates average value of concentration-dependent liquid D(C) taken at different diffusion time, the D(C) average value is more precise than that one single measured value.
The third positive result of the invention is the verification of obtained D(C). Based on the D(C), the diffusion images at any other time (ti≠t0) can be simulated by means of ray tracing method, the simulated diffusion images are compared with that of experimental images to verify the correctness of calculated D(C).
The invention, in one aspect, provides a work platform for acquiring diffusion images. The work platform, as shown in
The invention, in another aspect, provide an asymmetrical liquid cylindrical lens (ALCL) as shown in
The invention, in till another aspect, provides a method for treating diffusion images in order to obtain experimental concentration profile Ce(zj, t0). The method comprises four steps of (1) binarizing the diffusion image; (2) extracting image width as a feature parameter, and changing the parameter into related refractive index of diffusion solution; (3) changing the refractive index into related concentration of diffusion solution, and obtaining the profile of Ce(zj, t0); (4) calculating the concentration profile Ce(zj, t0) in the range of low concentration area, and obtaining the diffusion coefficient Do in the condition of infinite dilute solution, which is the boundary condition for solving numerically Fick diffusion equation.
The invention, in a further aspect, provides a method for solving numerically Fick diffusion equation, which can be written as
where D(C) is concentration dependent diffusion coefficient; C(z, t) is the concentration profile along the one-dimension diffusion direction z-axis at time t, D(C) can be expressed in the form of polynomial
D(C)=D0(1+α1C+α2C2+α3C3+ . . . ); (S-2)
where α1, α2, α3, . . . are the under-determined coefficients. Assuming z=0 to be interface between two diffusion solutions, initial concentrations in two sides of the interface be C1 and C2, and each solution height filled in the ALCL be H, the boundary conditions of Equation (S-1) satisfy with
A group of initial parameters [(α1)1, (α2)1, (α3)1] in Equation (D-2) are set by experience to determine D(C); the finite difference method (FDM)[1], the implicit FDM[2] and the chasing method[3] in computational mathematics are used to solve numerically the Equation (S-1) under the initial and boundary conditions of Equation (S-3). The calculated concentration spatial and temporal profile at any diffusion time are written as Cn(zj, t1), =0, 1, . . . M+1; i=0, 1, 2, . . . ).
The invention, in a further aspect, provides a method for determining under-determined coefficients (α1, α2, α3, . . . ) in Equation (S-2). The method compares the calculated C(zj, ti) with the experimental Ce(zj, t0), (j=0, 1, 2, . . . , M+1), and calculates standard deviation σk defined in Equation (S-4)
A series σks(k=2, 3, . . . , N) is calculated by varying under-determined parameters [(α1)k, (α2)k, (α3)k], the parameters corresponding the minimum value of σks are the best-fit parameters, which are selected to determine D(C)=D0[1+(α1)best×C+(α2)best×C2+(α3)best×C3+ . . . ].
The invention, in a further aspect, provides a method for verifying the correctness of calculated D(C). The method uses the FDM to solve numerically the Equation (S-1) to get the refractive index spatial and temporal profile nn(zj, ti) based on the calculated D(C); ray tracing method is applied to simulate diffusion image at any diffusion time, the simulated diffusion images are compared with the experimental diffusion images to verify the correctness of calculated D(C).
One object of the invention is to provide a novel optical method for measuring concentration-dependent liquid D(C) rapidly. The method requires only a single diffusion image taken at suitable time, which greatly shortens the experimental time required to measure concentration-dependent liquid D(C).
Another object of the invention is to provide a new method for measuring concentration-dependent liquid D(C) precisely. The method calculates average value of concentration-dependent liquid D(C) taken at different diffusion time, the D(C) average value is more precise than that one single measured value.
A further object of the invention is to provide a method for verifying the correctness of calculated D(C). Based on the calculated D(C), the diffusion images at any other time can be simulated by means of ray tracing method, the comparison between the simulated and experimental images give a direct verification of the calculated D(C).
These and other aspects, feature and advantages of the invention will be understood with reference to the drawing figures and detailed description herein, and will be realized by means of the various elements and combinations. It is to be understood that both the foregoing general description and the following brief description of the drawing and detailed description of the invention are exemplary and explanatory of preferred embodiments of the invention, and are not restrictive of the invention, as claimed.
The present invention may be understood more readily by reference to the following detailed description of the invention taken in connection with the accompanying drawing figures, which form a part of this description. It is to be understood that this inventions is not limited to the specific devices, methods, conditions or parameters described and/or shown herein, and that the terminology used herein is for the purpose of describing particular embodiments by way of example only and is not intended to be limiting of the claimed invention, Any and all patents and other publications identified in this specification are incorporated by reference as though fully set forth herein.
Measuring Concentration-Dependent Diffusion Coefficients of Ethylene Glycol Aqueous Solution at Room Temperature (298 K) Using an ALCL
The invention provides a work platform and a method for rapidly and precisely measuring concentration-dependent diffusion coefficients of binary solution. The work platform is used to acquire diffusion image, the method comprises: (1) acquiring experimental concentration profile Ce(zj, t0) from the diffusion images; (2) calculating concentration profile Cn(zj, t0) based on diffusion equation; (3) obtaining the concentration-dependent diffusion coefficient D(C) by comparing Ce(zj, t0) with Cn(zj, t0)s and (4) simulating diffusion images to verify the correctness of obtained D(C).
First of all, building up a work platform to acquire diffusion image.
The work platform for acquiring diffusion images is shown in
Furthermore, key device of the work platform is the ALCL, which acts as both diffusion pool and imaging optical element, the sizes of the ALCL are shown in FIG. (2). The length of the ALCL is L=50.0 mm, the material of the ALCL is BK9 glass of which refractive index is RI=n=n0=1.5163.
Furthermore, the characteristic parameters Δf (refractive index resolution), δf (measurement deviation of focal length) and δn (minimum resolved refractive index) are shown in
Furthermore, when collimated monochromatic light passing through the ALCL, the diffusion image appearing on the focal plane of the ALCL is a “beam waist” shaped image as shown in
Furthermore, the diffusion images are closely related to the diffusion time as shown in
Secondly, acquiring experimental concentration profile based on a diffusion image at room temperature (298 K).
The relationship between image-width (W) and refractive index (RI=n) of the solution filled in the core of the used ALCL is shown in
An experimental concentration profile is obtained from a diffusion image following the four steps: (1) binarizing the diffusion image of
the image-width profile We(z, t) is then changed into the RI=n profile ne(z, t) based on Equation (E-1), which is shown in
C(n)=10.16×n−13.542, (E-2)
the RI=n profile ne(x, t) is then changed into the concentration profile Ce(zj, t0) based on Equation (E-2), which is shown in FIG. (10d).
Thirdly, measuring diffusion coefficient in infinite dilute condition (D0).
Diffusion coefficient in infinite dilute condition (D0) is the boundary condition for solving numerically Fick diffusion Equation (E-3),
where C(z, t) represents the concentration profile along the one-dimension diffusion direction z-axis at time t, D(C) is the concentration-dependent coefficient that can be expressed in the form of polynomial as Equation (E-4),
D(C)=D0(1++α2C2+α3C3+ . . . ); (E-4)
where α1, α2, α3, . . . are the under-determined coefficients. Assuming z=0 to be interface between two diffusion solutions, initial concentrations in two sides of the interface be C1 and C2, and each solution height filled in the ALCL be H, boundary conditions satisfy with Equation (E-5),
The method introduced in reference[10] has been used to obtain D0 value, which fits the experimental concentration profile Ce(zj, t0) in the range of low concentration area with an analytical diffusion formula, and obtains the diffusion coefficient D0 in the condition of infinite dilute solution. For the EG aqueous solution at room temperature (298 K), measured results are listed in the third row of Table 1, the average value is D0=1.100×10−5 cm2/s.
Fourthly, solving Fick diffusion equation numerically using the algorithm of finite difference method.
The finite difference method (FDM)[10], the implicit FDM[11] and the chasing method[12] in computational mathematics are used to solve numerically the Equation (E-3) under the initial and boundary conditions of Equation (E-5). Assuming space step length along diffusion direction to be Δz=h, time step length to be Δt=τ, respectively, space and time coordinates become Equation (E-6),
z=z
j
=jΔ=jh, j=0, 1, 2, . . . M+1,
t=t
i
=iΔt=iτ, i=0, 1, 2, . . . . (E-6)
The spatial distance is divided into discrete M+2 terms, j=0 and M+1 are boundary terms. Equation (E-3) is changed to the difference quotient form in Equation (E-7),
Let rj−1i=τ×Dj−1i/h2, rji=τ×Dji/h2 and rj+1i=τ×Dj+1i/h2 for the convenience of calculation, Equation (E-7) is simplified as Equation (E-8),
Let Aji)/(rj+1i−rj−1i)/4 and Bji=rji; =Equation (E-8) is further simplified as Equation (E-9),
(Aji−Bji)Cj−1i+1+(1+2Bji)Cji+1+(Aji−Bji)Cj+1i+1Cji. (E-9)
Equation (E-9) is expanded into a series linear equations from j=1 to M
If Cji=0 s(j=0, 1, . . . M+1) are given by initial condition, C(z, t) at any diffusion time can be calculated numerically by solving Equation (E-10). In order to do it, Equation (E-10) is changed into the form of tridiagonal matrix Equation (E-11),
Where aji, bji and cji represent the coefficient terms before the Cj−1i+1, Cji+1 and Cj+1i+1 in the expanded linear equations, which can be expressed as
The initial and boundary conditions of Equation (E-5) are rewritten in discrete form as
Using the chasing method[11] in computational mathematics, and assuming a group of initial under-determined parameters [(α1)1, (α2)1, (α3)1] by experience to determine D(C), the concentration spatial and temporal profile Cn(zj, ti) can be obtained by calculating Equation (E-11) under the boundary conditions of Equation (E-13).
Fifthly, comparing Cn(zj, t0)s with Ce(zj, t0) to determine concentration-dependent diffusion coefficient D(C).
The calculated Cn(zj, ti) at a special moment ti=t0 min is used to compare with the experimental profile Ce(zj, t0), (j=0, 1, 2, . . . , M+1), and standard deviation σk defined in Equation (E-14) is calculated.
Using the same method, a series σks (k=2, 3, . . . , N) is then calculated by varying under-determined parameters [(α1)k, (α2)k, (α3)k], the parameters corresponding the minimum value of aks are the best-fit parameters, which are selected to determine D(C) in the form of polynomial, that is, D(C)=D0[1 (α1)best×C (α2)best×C2+(α3)best×C3+ . . . ].
For the EG aqueous solution at room temperature (298 K), measuring and fitting results of D(C) over a diffusion period from 240 to 285 min are listed in Table 1, where D0=1.100×10−5 cm2/s, (α1)best=−0.8558, (α2)best=0.0016 and (α3)best=0.000003, leading to an average value of D(C)=1.100×10−5(1−0.8558C+0.0016C2+0.000003C3) cm2/s
Sixthly, verifying the calculated D(C) by comparing it with literature values.
The obtained concentration-dependent diffusion coefficient D(C) by the present invention has been compared with the reported values of J. Ferna′ndez-Sempere (1996)[13] and Bogachev (1982)[14] for the same diffusion solution and at the same temperature (298K), the comparison results are shown in
Seventhly, verifying the calculated D(C) by simulating diffusion image at any diffusion time.
Based on the calculated D(C) and Equations (E-10) to (E-13), the FDM is used to solve numerically the diffusion Equation (E-3) to get the concentration spatial and temporal profile Cn(zj, ti)s(j=0, 1, . . . M+1; i=0, 1, 2, . . . ), which are changed into the RI profile nn(z, t) based on Equation (E-2). In order to verify the correctness of calculated D(C), ray tracing method is applied to simulate diffusion image at any diffusion time, the simulation calculation flow-chart of the ray tracing method is shown in
The simulated diffusion images are compared with the experimental diffusion images when CMOS is fixed on different positions, which are shown in
While the invention has been described with reference to preferred and example embodiments, it will be understood by those skilled in the art that a variety of modifications, additions and deletions are within the scope of the invention, as defined by the following claims.
Number | Date | Country | Kind |
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201811250468.0 | Oct 2018 | CN | national |