Development of a Portable Residual Chlorine Detection Device with a Combination of Microfluidic Chips and LS-BP Algorithm to Achieve Accurate Detection of Residual Chlorine in Water
Abstract
:1. Introduction
2. Experiments
2.1. Development of the Portable Residual Chlorine Detection Device
2.2. Methods
2.3. Fluid Simulation Mechanics Model
2.4. Parameterized Design of Microfluidic Chip
2.5. The Principle of Dual-Channel Signal Reading Method
2.6. The Construction of LS-BP Algorithm
2.7. Characterizations
3. Results and Discussion
3.1. Influence of Microfluidic Chip Structure Parameters on Liquid Mixing Efficiency of Microfluidic Chip
3.2. Calibration and Evaluation of the LS-BP Algorithm
3.3. Performance Evaluation the Portable Residual Chlorine Detection Device
4. Conclusions
- (1)
- A microfluidic chip that can achieve efficient mixing of two-phase flow was studied. The results indicate that channel amplitude A, channel width α, the channel angular frequency ω have an impact on mixing efficiency. The increase in channel amplitude A and channel width α is beneficial for improving the mixing efficiency, and the increase in channel width is not beneficial for improving the mixing efficiency. When channel amplitude A is 1.8 mm, channel width α is 0.7 mm, and channel angular frequency ω is 0.7 rad·s−1, the microfluidic chip has good mixing efficiency. This microfluidic chip can also be used for liquid mixing in other detection devices, reducing device volume and cost and achieving efficient and fast mixing.
- (2)
- An LS-BP algorithm was proposed, which is based on the least squares method and the BP neural network. The LS-BP algorithm was used to predict the residual chlorine concentration in water, and it has good accuracy. The average absolute percentage error of the prediction result is 0.24%, the average of the prediction residuals is 1.781 × 10−4 mg·L−1, and the variance of the prediction residuals is 1.019 × 10−4. This algorithm is also applicable to the detection of other substances in water and still has good detection accuracy and reliability, which will be further confirmed in future research.
- (3)
- The limit of detection of the portable residual chlorine detection device is 0.01 mg·L−1, the relative standard deviation is 3.2%, the detection reagent is 50 s, the detection liquid consumption is 5 mL, and the construction and maintenance costs are low. Compared with other residual chlorine detection devices and methods, the portable residual chlorine detection device has relatively high detection accuracy, fast detection speed, a low cost, and is more convenient. The portable residual chlorine detection fills the gap in the absence of a device that can accurately, rapidly, and conveniently detect residual chlorine in water at low cost. It can also be used to detect residual chlorine in other types of water, such as drinking water, swimming pools, and aquaculture. It can also be used to detect residual chlorine in water, such as drinking water, swimming pools, and aquaculture.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- : 0.41161614839205645788.~:
141.2504359 | −141.4011525 | −141.2519994 | −141.1017039 |
−141.4005702 | 141.4032695 | 141.3956747 | 141.4083927 |
141.0236009 | 141.2978612 | −141.3844829 | 141.4103766 |
141.2957102 | −141.4001036 | 141.4004749 | 141.2828344 |
−141.3854709 | 141.3970232 | 141.3189413 | −141.3990541 |
141.4186759 | 141.4169222 | −141.3299803 | 141.3952249 |
−141.5110642 | 141.4827488 | 141.4018716 | 141.3853218 |
141.4001668 | −141.4079296 | 141.4004309 | 141.3478875 |
141.4001615 | 141.3980244 | −141.4000452 | −141.4001779 |
141.387853 | −141.4248409 | 141.3097943 | −141.3686975 |
−141.4009656 | 141.3900592 | −141.4443236 | −141.6041695 |
141.3983219 | 141.4583967 | −141.364381 | −141.4000618 |
141.399928 | −141.4044611 | −141.3999539 | −141.3983195 |
141.4000332 | −141.4164973 | 141.3796423 | −141.3935644 |
141.4000267 | 141.3997814 | −141.3260521 | 141.3830316 |
−141.3973547 | 141.4085095 | −141.42645 | 141.3979954 |
−141.407349 | 141.7166441 | −141.4025577 | −141.3686268 |
−141.400651 | −141.4022892 | 141.4186206 | 141.3995125 |
141.6725111 | −141.0832039 | −141.158267 | 141.4372117 |
141.3955837 | −141.4321767 | 141.3992102 | −141.394406 |
−141.3997424 | 141.4889649 | −141.3986888 | −141.3880851 |
141.3999823 | 141.3815284 | −141.4059837 | 141.3999221 |
−141.5019556 | 141.4128821 | 141.4047919 | −141.3550851 |
141.4107121 | 141.3992634 | −141.4421445 | 141.3994286 |
−141.3928016 | 141.1657684 | 141.3325874 | −141.3782855 |
141.3725278 |
- :
−141.5495561 | 138.5708301 | 135.8964638 | 133.235595 |
130.0873913 | −127.2563324 | −124.436855 | −121.5940636 |
−119.2216235 | −116.0728196 | 113.1396911 | −110.2783401 |
−107.6002023 | 104.6358647 | −101.8073212 | −99.14758398 |
96.17323234 | −93.32856251 | −90.62383791 | 87.66946598 |
−84.80842559 | −81.97993806 | 79.30590321 | −76.36422959 |
73.30863683 | −70.53344465 | −67.86826849 | −65.07673529 |
−62.21579933 | 59.36866503 | −56.5587362 | −53.86808869 |
−50.90359181 | −48.08181082 | 45.24786731 | 42.41936384 |
−39.63526448 | 36.66826693 | −34.2976312 | 31.24359466 |
28.27499773 | −25.50520587 | 22.32961384 | 18.32477447 |
−16.97813676 | −13.53824327 | 11.74839349 | 8.483250626 |
−5.659624622 | 2.612233058 | 0.002251331 | −2.911993138 |
5.655440015 | −8.222086563 | 11.56792804 | −14.20761648 |
16.96771089 | 19.79735228 | −23.08138955 | 25.54066884 |
−28.28966285 | 31.06715509 | −33.82812811 | 36.77241999 |
−39.56654237 | 41.37493514 | −45.23900074 | −48.17033354 |
−50.90221943 | −53.72581801 | 56.51344655 | 59.38911505 |
61.59619678 | −65.73613663 | −68.37304638 | 70.62467543 |
73.53619354 | −76.29577652 | 79.18539572 | −82.02150246 |
−84.83987405 | 87.52412377 | −90.49802383 | −93.34243536 |
96.15202239 | 99.00606884 | −101.7995551 | 104.636101 |
−107.331144 | 110.2747684 | 113.1139801 | −116.0031577 |
118.7632339 | 121.6048347 | −124.3839212 | 127.2606559 |
−130.0956288 | 133.1645088 | 135.8147141 | −138.5941523 |
141.4274495 |
- ~:
−0.408021378 | −0.443543769 | −0.056335236 | 1.243239318 |
−0.310122142 | 0.300648953 | 0.593338153 | 0.007278645 |
0.128387282 | −0.563887928 | −0.365094037 | −0.307482713 |
−0.347895502 | −0.244299305 | 0.542084498 | −0.53668857 |
−0.278636514 | 0.256257735 | −0.615811048 | 0.031489501 |
−0.003125461 | 0.083573569 | 0.039385908 | 0.031559314 |
0.109285293 | −0.847466461 | 0.415077107 | 1.096385917 |
−0.169989554 | −0.140180424 | −0.147377868 | −0.709785718 |
0.419910783 | 0.278136388 | 0.340071645 | −0.363530583 |
−0.3105091 | −0.276485619 | −0.403120783 | 0.403613555 |
−1.161046593 | −0.17926388 | 0.186199256 | 0.0951566 |
0.272933021 | −0.422003839 | 0.859023141 | −0.350774225 |
0.865547226 | 0.544845546 | −0.009180248 | −0.698944579 |
−0.182356484 | 0.122776662 | −0.220465477 | −0.042581537 |
0.129502124 | 0.273089463 | 0.640719077 | 0.072663101 |
0.037388001 | 0.663734977 | 0.866908298 | 1.134975611 |
−0.247231174 | −1.293483487 | −0.318937347 | 0.660760734 |
−0.592936776 | −0.322754685 | −0.249880331 | 0.278402255 |
−0.223195168 | −0.336272916 | 0.433576425 | −0.122935128 |
0.082938506 | −0.331092961 | −0.304524849 | 0.141388732 |
−0.234651742 | −0.443891949 | −0.243050278 | 0.906897894 |
0.299165659 | 0.689577838 | 0.360606383 | 0.023650694 |
−0.795369982 | 0.005526423 | −0.446438409 | −0.341361915 |
−0.275427569 | 0.160497631 | 0.358515343 | 1.289239667 |
0.305641027 | −0.911761932 | 0.227717189 | −0.078282132 |
−0.078005239 |
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Sample Number | Detection Results of Blank Samples (mg·L−1) |
---|---|
1 | 0.012 |
2 | 0.011 |
3 | 0.008 |
4 | 0.007 |
5 | 0.009 |
6 | 0.008 |
7 | 0.011 |
8 | 0.014 |
9 | 0.012 |
10 | 0.011 |
Concentration of Residual Chlorine Standard Solution (mg·L−1) | First Detection | Second Detection | Third Detection |
---|---|---|---|
1 | 1.075 | 1.067 | 1.013 |
2 | 2.116 | 2.054 | 1.987 |
3 | 3.097 | 3.168 | 3.029 |
4 | 4.133 | 4.197 | 4.031 |
5 | 5.141 | 5.236 | 4.938 |
6 | 5.841 | 6.035 | 6.138 |
7 | 7.275 | 7.232 | 6.931 |
8 | 8.027 | 8.119 | 7.891 |
9 | 9.217 | 9.128 | 8.831 |
10 | 9.832 | 10.241 | 10.145 |
Item | Limit of Detection (mg·L−1) | Relative Standard Deviation | Detection Range (mg·L−1) | Detection Time (min) | Consumption of Detection Reagents (mL) |
---|---|---|---|---|---|
Sargazi et al., + 2020 [59] | 0.05 | 8.75% | 1–4 | 2 | 5 |
Uriarte et al., + 2021 [60] | 0.006 | 4.6% | 0.02–0.5 | -- | 5 |
Dou et al., + 2020 [23] | 0.161 | -- | 0.56–9.8 | 30 | 5 |
Yen et al., + 2019 [24] | 0.18 | -- | 0.1–500 | 5 | -- |
Kato et al., + 2017 [61] | 0.1 | -- | 0.3–1 | 4 | -- |
Huangfu et al., + 2019 [62] | 0.2 | -- | 0.2–5 | -- | 10 |
Xiong et al., + 2015 [63] | 0.035 | 4.2 | 0.056–56 | 20 | 0.12 |
Lu et al., + 2016 [64] | 0.028 | -- | 0.035–10.5 | 5 | 100 |
The portable residual chlorine detection device in this study | 0.01 | 3.2% | 0–10 | 0.8 | 5 |
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Wang, T.; Niu, J.; Pang, H.; Meng, X.; Sun, R.; Xie, J. Development of a Portable Residual Chlorine Detection Device with a Combination of Microfluidic Chips and LS-BP Algorithm to Achieve Accurate Detection of Residual Chlorine in Water. Micromachines 2024, 15, 1045. https://doi.org/10.3390/mi15081045
Wang T, Niu J, Pang H, Meng X, Sun R, Xie J. Development of a Portable Residual Chlorine Detection Device with a Combination of Microfluidic Chips and LS-BP Algorithm to Achieve Accurate Detection of Residual Chlorine in Water. Micromachines. 2024; 15(8):1045. https://doi.org/10.3390/mi15081045
Chicago/Turabian StyleWang, Tongfei, Jiping Niu, Haoran Pang, Xiaoyu Meng, Ruqian Sun, and Jiaqing Xie. 2024. "Development of a Portable Residual Chlorine Detection Device with a Combination of Microfluidic Chips and LS-BP Algorithm to Achieve Accurate Detection of Residual Chlorine in Water" Micromachines 15, no. 8: 1045. https://doi.org/10.3390/mi15081045