Method and system for measuring chloride ion content in water sample, electronic device, and storage medium
The chloride ion content detection method established by training a neural network model uses pressure and flow sensors to acquire parameters, achieving efficient and accurate detection of chloride ion content in water samples and solving the problem of large detection errors in existing technologies.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- XILINGOL THERMAL POWER CO LTD
- Filing Date
- 2025-11-06
- Publication Date
- 2026-07-23
AI Technical Summary
The accuracy and repeatability of chloride ion content detection in water samples in existing technologies are limited by the precision of experimental equipment and manual operation, resulting in large errors and low efficiency in experimental results.
A chloride ion content detection model was established by training a neural network model. Water sample test parameters were obtained through pressure and flow sensors. The chloride ion content was calculated using machine learning algorithms and then visualized.
It reduced the workload of laboratory technicians, improved testing efficiency, reduced the error of test results, and improved the accuracy of experimental results.
Smart Images
Figure CN2025133061_23072026_PF_FP_ABST
Abstract
Description
Methods, systems, electronic equipment and storage media for detecting chloride ion content in water samples Technical Field
[0001] The embodiments disclosed herein belong to the field of water sample chloride ion content detection technology, specifically relating to a water sample chloride ion content detection method, system, electronic device and storage medium. Background Technology
[0002] In thermal power generation, the laboratory is an indispensable and crucial link in the power plant's production process. Laboratory work directly impacts the normal operation of production equipment and the stability of product quality. The laboratory is responsible for various environmental protection experiments related to water, coal, and oil throughout the plant. For example, the silver nitrate titration method (Mohr's method) is used to detect the chloride ion content in water samples. This requires manual operation of instruments such as burettes and graduated cylinders, recording data, and calculating the test results to determine whether the chloride ion content in the water sample exceeds the standard value.
[0003] In laboratory work, due to the limitations of experimental equipment and the precision of manual operation, the accuracy and repeatability of experimental results are affected. Laboratory technicians need to take samples and recheck multiple times to reduce errors in the experiment, which is a lot of work, inefficient, and the accuracy of experimental results is not high enough. Summary of the Invention
[0004] The embodiments disclosed herein aim to at least solve one of the technical problems existing in the prior art, and provide a method, system, electronic device and storage medium for detecting chloride ion content in water samples.
[0005] One aspect of this disclosure provides a method for detecting chloride ion content in a water sample, the method comprising:
[0006] Obtain water sample test parameters;
[0007] The water sample test parameters are input into a pre-established chloride ion content detection model, and the chloride ion content is output; wherein, the chloride ion content detection model is trained using a neural network model based on the water sample test dataset;
[0008] The chloride ion content is then visualized and output.
[0009] Furthermore, the acquisition of water sample testing parameters includes:
[0010] Water sample testing parameters are obtained using pressure sensors and flow sensors; among them,
[0011] The water sample testing parameters include at least the amount of silver nitrate standard solution consumed in the water sample titration, the amount of silver nitrate standard solution consumed in the blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample.
[0012] Furthermore, the chloride ion content detection model is pre-established through the following steps:
[0013] Obtain a water sample test dataset; wherein the water sample test dataset includes the consumption of silver nitrate standard solution in water sample titration, the consumption of silver nitrate standard solution in blank titration, the concentration of silver nitrate standard solution, the volume of water sample, and the chloride ion content;
[0014] The consumption of silver nitrate standard solution in the water sample titration, the consumption of silver nitrate standard solution in the blank titration, the concentration of silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as the output to train the neural network model to obtain the chloride ion content detection model.
[0015] Furthermore, the chloride ion content in the water sample test dataset is calculated using the following formula:
[0016] In the formula, X is the chloride ion content, a is the amount of silver nitrate standard solution consumed in the titration of the water sample, b is the amount of silver nitrate standard solution consumed in the blank titration, T is the concentration of the silver nitrate standard solution, and V is the volume of the water sample.
[0017] Another aspect of this disclosure provides a system for detecting chloride ion content in water samples, the system comprising:
[0018] The data acquisition module is used to acquire water sample test parameters;
[0019] The data processing module is used to input the water sample test parameters into a pre-established chloride ion content detection model and output the chloride ion content; wherein, the chloride ion content detection model is trained using a neural network model based on the water sample test dataset;
[0020] The data output module is used to visualize the chloride ion content.
[0021] Furthermore, the data acquisition module includes a pressure sensor and a flow sensor;
[0022] The water sample testing parameters include at least the amount of silver nitrate standard solution consumed in the water sample titration, the amount of silver nitrate standard solution consumed in the blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample.
[0023] Furthermore, the system also includes a model building module; the model building module is specifically used for:
[0024] Obtain a water sample test dataset; wherein the water sample test dataset includes the consumption of silver nitrate standard solution in water sample titration, the consumption of silver nitrate standard solution in blank titration, the concentration of silver nitrate standard solution, the volume of water sample, and the chloride ion content;
[0025] The consumption of silver nitrate standard solution in the water sample titration, the consumption of silver nitrate standard solution in the blank titration, the concentration of silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as the output to train the neural network model to obtain the chloride ion content detection model.
[0026] Furthermore, the chloride ion content in the water sample test dataset is calculated using the following formula:
[0027] In the formula, X is the chloride ion content, a is the amount of silver nitrate standard solution consumed in the titration of the water sample, b is the amount of silver nitrate standard solution consumed in the blank titration, T is the concentration of the silver nitrate standard solution, and V is the volume of the water sample.
[0028] Another aspect of this disclosure provides an electronic device, comprising:
[0029] At least one processor; and,
[0030] A memory communicatively connected to the at least one processor is used to store one or more programs that, when executed by the at least one processor, enable the at least one processor to implement the water sample chloride ion content detection method described above.
[0031] Another aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for detecting chloride ion content in water samples described above.
[0032] This disclosure discloses a method, system, electronic device, and storage medium for detecting chloride ion content in water samples. By employing machine learning to establish a chloride ion content detection model and using the model to obtain chloride ion content detection results, it reduces the workload of laboratory technicians, improves experimental efficiency, reduces the error of detection results, and provides a reference for the accuracy of manually operated experimental results. Attached Figure Description
[0033] Figure 1 is a schematic flowchart of a method for detecting chloride ion content in water samples according to an embodiment of this disclosure;
[0034] Figure 2 is a schematic diagram of a water sample chloride ion content detection system according to another embodiment of the present disclosure;
[0035] Figure 3 is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. Detailed Implementation
[0036] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.
[0037] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.
[0038] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0039] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this disclosure. As used in this disclosure, the term "and / or" includes all combinations of any and more of the associated listed items.
[0040] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing this disclosure, and therefore cannot be used to limit the scope of protection of this disclosure.
[0041] As shown in Figure 1, one embodiment of this disclosure provides a method for detecting chloride ion content in water samples, including:
[0042] Step S1: Obtain water sample test parameters.
[0043] Specifically, pressure sensors, flow sensors, and other methods are used to obtain parameters for water sample titration tests. The water sample test parameters include at least the amount of silver nitrate standard solution consumed in the water sample titration (a), the amount of silver nitrate standard solution consumed in the blank titration (b), the concentration of silver nitrate standard solution (T), and the volume of the water sample (V).
[0044] Wherein, the consumption of silver nitrate standard solution 'a' in the water sample titration refers to the volume of silver nitrate standard solution added to the water sample until precipitation occurs, and the consumption of silver nitrate standard solution 'b' in the blank titration refers to the volume of silver nitrate standard solution added to distilled water of the same volume as the water sample until precipitation occurs, which can be detected in real time using a flow sensor; the concentration T of the silver nitrate standard solution needs to be calibrated using a sodium chloride standard solution; and the water sample volume V refers to the volume of the water sample taken in the titration experiment, which can be detected using a pressure sensor.
[0045] Step S2: Input the water sample test parameters into the pre-established chloride ion content detection model and output the chloride ion content.
[0046] Specifically, the chloride ion content detection model is pre-established through the following steps:
[0047] 1. Obtain the water sample test dataset.
[0048] A large amount of historical data from water sample chloride ion content detection titration experiments was obtained, including the consumption of silver nitrate standard solution in water sample titration, the consumption of silver nitrate standard solution in blank titration, the concentration of silver nitrate standard solution, the volume of water sample, and the corresponding chloride ion content detection results for each experiment, resulting in a dataset composed of a large amount of labeled data.
[0049] The chloride ion content in the water sample test dataset can be calculated using the following formula:
[0050] In the formula, X represents chloride ions (Cl... - The values are: content (mg / L), a is the volume of silver nitrate standard solution consumed in the titration of the water sample (ml), b is the volume of silver nitrate standard solution consumed in the blank titration (ml), T is the concentration of silver nitrate standard solution (mg / L), and V is the volume of the water sample (ml).
[0051] 2. Using the consumption of silver nitrate standard solution in the water sample titration, the consumption of silver nitrate standard solution in the blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample as inputs, and the chloride ion content as output, the neural network model is trained to obtain a chloride ion content detection model.
[0052] Specifically, the water sample test dataset is divided into a training set and a validation set. Parameters such as learning rate, batch size, and number of iterations are set. The neural network model is trained using the training set and the accuracy of the model is verified using the validation set. The neural network model that meets the requirements is used as the chloride ion content detection model.
[0053] Input the water sample test parameters obtained in step S1 into the trained chloride ion content detection model, and the model will output the corresponding chloride ion content.
[0054] Step S3: Visualize and output the chloride ion content.
[0055] Specifically, the chloride ion content results obtained from the model output in the previous step S2 are displayed using methods such as liquid crystal displays, printing, and data entry into storage media, so that experimenters can easily query and compare them.
[0056] This disclosure discloses a method for detecting chloride ion content in water samples. By employing machine learning to establish a chloride ion content detection model and using the model to obtain chloride ion content detection results, the method reduces the workload of laboratory technicians, improves experimental efficiency, reduces the error of detection results, and provides a reference for the accuracy of manually operated experimental results.
[0057] As shown in Figure 2, another embodiment of this disclosure provides a water sample chloride ion content detection system, including:
[0058] Data acquisition module 210 is used to acquire water sample test parameters;
[0059] Data processing module 220 is used to input the water sample test parameters into a pre-established chloride ion content detection model and output the chloride ion content; wherein, the chloride ion content detection model is trained using a neural network model based on the water sample test dataset;
[0060] The data output module 230 is used to visualize the chloride ion content.
[0061] For example, the data acquisition module includes a pressure sensor and a flow sensor;
[0062] The water sample testing parameters include at least the amount of silver nitrate standard solution consumed in the water sample titration, the amount of silver nitrate standard solution consumed in the blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample.
[0063] For example, as shown in FIG2, the system further includes a model building module 240; the model building module 240 is specifically used for:
[0064] Obtain a water sample test dataset; wherein the water sample test dataset includes the consumption of silver nitrate standard solution in water sample titration, the consumption of silver nitrate standard solution in blank titration, the concentration of silver nitrate standard solution, the volume of water sample, and the chloride ion content;
[0065] The consumption of silver nitrate standard solution in the water sample titration, the consumption of silver nitrate standard solution in the blank titration, the concentration of silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as the output to train the neural network model to obtain the chloride ion content detection model.
[0066] For example, the chloride ion content in the water sample test dataset is calculated using the following formula:
[0067] In the formula, X is the chloride ion content, a is the amount of silver nitrate standard solution consumed in the titration of the water sample, b is the amount of silver nitrate standard solution consumed in the blank titration, T is the concentration of the silver nitrate standard solution, and V is the volume of the water sample.
[0068] Specifically, a water sample chloride ion content detection system according to an embodiment of this disclosure is used to implement the water sample chloride ion content detection method described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.
[0069] This disclosure discloses a water sample chloride ion content detection system that uses machine learning to establish a chloride ion content detection model and uses the model to obtain chloride ion content detection results. This reduces the workload of laboratory technicians, improves experimental efficiency, reduces the error of detection results, and provides a reference for the accuracy of manually operated experimental results.
[0070] As shown in Figure 3, another embodiment of this disclosure provides an electronic device, including:
[0071] At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301 for storing one or more programs that, when executed by the at least one processor 301, enable the at least one processor 301 to implement the water sample chloride ion content detection method described above.
[0072] The memory 302 and processor 301 are connected via a bus, which can include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 301 and memory 302 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 301 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 301.
[0073] Processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 302 can be used to store data used by processor 301 during operation.
[0074] Another embodiment of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the water sample chloride ion content detection method described above.
[0075] The computer-readable storage medium may be included in the systems or electronic devices disclosed herein, or it may exist independently.
[0076] Computer-readable storage media can be any tangible medium that contains or stores a program, and can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, optical fibers, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0077] Computer-readable storage media may also include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.
[0078] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.
Claims
1. A method for detecting chloride ion content in water samples, characterized in that, The method includes: Obtain water sample test parameters; The water sample test parameters are input into a pre-established chloride ion content detection model, and the chloride ion content is output; wherein, the chloride ion content detection model is trained using a neural network model based on the water sample test dataset; The chloride ion content is then visualized and output.
2. The method according to claim 1, characterized in that, The acquisition of water sample testing parameters includes: Water sample testing parameters are obtained using pressure sensors and flow sensors; among them, The water sample testing parameters include at least the amount of silver nitrate standard solution consumed in the water sample titration, the amount of silver nitrate standard solution consumed in the blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample.
3. The method according to claim 1, characterized in that, The chloride ion content detection model is pre-established through the following steps: Obtain a water sample test dataset; wherein the water sample test dataset includes the consumption of silver nitrate standard solution in water sample titration, the consumption of silver nitrate standard solution in blank titration, the concentration of silver nitrate standard solution, the volume of water sample, and the chloride ion content; The consumption of silver nitrate standard solution in the water sample titration, the consumption of silver nitrate standard solution in the blank titration, the concentration of silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as the output to train the neural network model to obtain the chloride ion content detection model.
4. The method according to claim 3, characterized in that, The chloride ion content in the water sample test dataset is calculated using the following formula: In the formula, X is the chloride ion content, a is the amount of silver nitrate standard solution consumed in the titration of the water sample, b is the amount of silver nitrate standard solution consumed in the blank titration, T is the concentration of the silver nitrate standard solution, and V is the volume of the water sample.
5. A system for detecting chloride ion content in water samples, characterized in that, The system includes: The data acquisition module is used to acquire water sample test parameters; The data processing module is used to input the water sample test parameters into a pre-established chloride ion content detection model and output the chloride ion content; wherein, the chloride ion content detection model is trained using a neural network model based on the water sample test dataset; The data output module is used to visualize the chloride ion content.
6. The system according to claim 5, characterized in that, The data acquisition module includes a pressure sensor and a flow sensor; The water sample testing parameters include at least the amount of silver nitrate standard solution consumed in the water sample titration, the amount of silver nitrate standard solution consumed in the blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample.
7. The system according to claim 5, characterized in that, The system also includes a model building module; the model building module is specifically used for: Obtain a water sample test dataset; wherein the water sample test dataset includes the consumption of silver nitrate standard solution in water sample titration, the consumption of silver nitrate standard solution in blank titration, the concentration of silver nitrate standard solution, the volume of water sample, and the chloride ion content; The consumption of silver nitrate standard solution in the water sample titration, the consumption of silver nitrate standard solution in the blank titration, the concentration of silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as the output to train the neural network model to obtain the chloride ion content detection model.
8. The system according to claim 7, characterized in that, The chloride ion content in the water sample test dataset is calculated using the following formula: In the formula, X is the chloride ion content, a is the amount of silver nitrate standard solution consumed in the titration of the water sample, b is the amount of silver nitrate standard solution consumed in the blank titration, T is the concentration of the silver nitrate standard solution, and V is the volume of the water sample.
9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs that, when executed by the at least one processor, enable the at least one processor to implement the method for detecting chloride ion content in water samples as described in any one of claims 1 to 4.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for detecting chloride ion content in water samples as described in any one of claims 1 to 4.