LAS (linear alkylbenzenesulfonate sodium) is a typical pollutant in industrial and municipal wastewater, and the stable operation of its online monitoring equipment is the key to water quality control. To address the pain points of high on-site operation and maintenance costs and delayed fault response of traditional monitoring equipment, building a remote operation and intelligent warning system can achieve full process control. Its core implementation path can be divided into three modules: "data interconnection", "intelligent diagnosis", and "warning linkage", which are suitable for the large-scale application of domestic monitoring equipment.
1、 Data Interconnection: Building a Full Link Remote Communication Architecture
The foundation of remote operation and maintenance is the real-time transmission of monitoring data and device status, which requires the establishment of a data link between the "device edge end cloud".
1. Terminal data collection: inOnline monitoring of LAS surfactantsMulti dimensional sensors are embedded in the system, which not only collect LAS concentration data, but also synchronously monitor the operating parameters of the equipment such as pump speed, reagent residue, and optical path cleanliness. The data is integrated through the 485 bus; For outdoor network free scenarios, it is equipped with NB IoT or LoRa modules to achieve low-power data transmission, ensuring uninterrupted data from remote monitoring points.
2. Edge end pre-processing: deploy edge computing gateways at monitoring stations to filter and denoise the collected original data, eliminate invalid data caused by electromagnetic interference, and at the same time convert the equipment operation parameters and LAS concentration data to protocol (such as Modbus to MQTT) to reduce the cloud data processing pressure and ensure transmission efficiency.
3. Cloud platform integration: Build a unified IoT management platform, connect data from various monitoring stations, achieve visual display of LAS concentration curves and device operating status, support hierarchical management of permissions, and allow operation and maintenance personnel to remotely view real-time device conditions through computers or mobile devices, without the need to be present to grasp the monitoring overview.
2、 Intelligent diagnosis: Establishing a device fault prediction model
The core of intelligent warning is to achieve early identification of faults through algorithms, rather than passive response.
1. Feature parameter calibration: Based on historical operation and maintenance data, calibrate the typical features of equipment failures, such as detection interruption caused by reagent remaining below 10%, data drift caused by light path transmittance below 85%, and mechanical failure predicted by abnormal fluctuations in pump body current. Convert these features into algorithm recognition thresholds.
2. AI algorithm modeling: Integrating machine learning models into cloud platforms, automatically identifying abnormal trends through continuous learning of device operating parameters. For example, when the light path transmittance decreases at a rate of 3% per day, the model can predict that a detection fault will occur 5 days later and trigger a cleaning reminder in advance; If the coefficient of variation of LAS detection data suddenly increases, it can be automatically determined as reagent failure or sensor drift, and the self-test program can be initiated.
3. Remote diagnosis linkage: When the model recognizes abnormalities, it can remotely issue self checking instructions to control equipment for basic maintenance such as zero calibration and pipeline flushing; For faults that cannot be remotely resolved, automatically generate operation and maintenance work orders, label the fault location, cause, and solution, and push them to the operation and maintenance personnel terminal to shorten the fault location time.
3、 Warning linkage: achieving multi-level response and risk control
Intelligent warning needs to be linked with business scenarios, taking into account equipment operation and water quality risk control.
1. Grading warning mechanism: setting dual warning thresholds, the equipment level is divided into three levels: "minor abnormalities (such as insufficient reagent remaining)", "moderate faults (such as light path pollution)", and "severe faults (such as sensor damage)", which trigger reminders, remote intervention, and on-site repair instructions respectively; At the water quality level, when the warning value of LAS concentration exceeds the standard, the alarm is synchronously pushed to the environmental supervision end and the enterprise operation and maintenance end to achieve rapid response to pollution risks.
2. Closed loop operation and maintenance management: Establish a closed-loop process of warning disposal feedback. After the operation and maintenance personnel complete the fault handling, they need to upload the disposal record on the platform. The model will optimize the warning threshold based on the disposal effect, form a self iterative operation and maintenance system, and improve the accuracy of subsequent predictions.
3. Localization adaptation optimization: Based on the hardware characteristics of domestic LAS monitoring equipment, customized communication protocols and algorithm models are developed to reduce the threshold for equipment modification. At the same time, relying on localized cloud platforms, low latency data transmission is achieved to adapt to the complex monitoring network environment in China.
Through the above path,Online monitoring of LAS surfactantsThe transformation from "manual inspection" to "remote intelligent control" can be achieved, which not only reduces operation and maintenance costs, but also improves the efficiency of water quality risk prevention and control, providing a replicable solution for the intelligent upgrade of water environment monitoring.