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Water Treatment Plant Quality and Process Monitoring Dashboard with Dashtera
- Published
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Software Developer Janaka Alwis
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1. Introduction
Water treatment plants require continuous monitoring of water-quality and process parameters to maintain stable treatment performance and identify abnormal conditions. Key indicators such as pH, turbidity, total dissolved solids (TDS), conductivity, chloramines, trihalomethanes (THMs), temperature, and flow rate provide insight into both final-water quality and treatment effectiveness.
This project presents a Water Treatment Plant Quality and Process Monitoring Dashboard developed using Dashtera with InfluxDB Cloud 3 as the backend. A water-potability dataset was extended into a live-streaming time-series dataset representing measurements across intake, coagulation, filtration, disinfection, and final treatment.
A Python streaming service deployed on an Oracle Cloud Always Free VM runs continuously as a self-restarting systemd service, sending timestamped records from the CSV dataset to InfluxDB Cloud 3. Dashtera then transforms the data into KPIs, gauges, time-series charts, statistical distributions, regression analysis, Pareto charts, and 3D visualizations.
The solution consists of three dashboard pages covering plant overview, treatment-process monitoring, and alarm-focused advanced analytics.
Source: Water Potability — Kaggle
2. Dashboard Objectives
The primary objective is to develop an interactive, near-real-time dashboard for monitoring water quality, treatment performance, operating conditions, and abnormal events.
Objectives
- Monitor water-quality score, flow rate, pH, plant status, and critical events.
- Track final turbidity, TDS, conductivity, chloramines, and THMs.
- Compare pH, turbidity, and TDS across treatment stages.
- Evaluate treatment performance through turbidity reduction efficiency.
- Analyse warning, critical, and alarm events.
- Investigate relationships between water-quality variables using regression and 3D analysis.
- Demonstrate Dashtera’s capability for streaming industrial BI with InfluxDB Cloud 3.
The dashboard is organised into three pages:
- Water Treatment Plant Overview — plant KPIs, trends, status, and operating conditions.
- Water Quality & Process Monitoring — treatment-stage performance and water-quality analysis.
- Water Quality, Alarms & Advanced Analytics — alarms, regression, distributions, and multidimensional analysis.
3. Dataset Description
The dashboard uses a live-streaming time-series dataset simulating water-treatment plant operations. The original Water Potability dataset was extended with treatment-stage measurements, operational parameters, derived quality indicators, and alarm information.
The main variables include:
- Treatment stages: Intake, Coagulation, Filtration, Disinfection, Final Treatment
- Water quality: pH, turbidity, TDS, conductivity, chloramines, THMs
- Operations: Water temperature and flow rate
- Quality indicators: Quality score and water-quality status
- Events: Warning count, critical count, plant status, and alarm type
Alarm categories include High Turbidity, Elevated TDS, Low Chloramine, High Conductivity, pH Out of Range, and Critical Water Quality.
The streaming architecture converts the prepared CSV dataset into a continuously updating time-series source. A Python application deployed on an Oracle Cloud Always Free VM reads the source data and writes timestamped observations to InfluxDB Cloud 3, operating as a systemd-managed service with automatic restart capability.
The resulting variable diversity supports plant-level monitoring, treatment-stage comparison, water-quality analysis, alarm detection, statistical analysis, and multidimensional process diagnostics.
4. Dashtera Platform Overview
Dashtera is a no-code business intelligence platform that supports time-series analytics and advanced data visualization.
Key Features
- Wide range of visualizations, including KPI panels, gauges, line charts, area charts, histograms, regression charts, Pareto charts, and 3D charts.
- Integration with time-series data sources including InfluxDB Cloud 3.
- Real-time or near-real-time querying and dashboard refresh.
- Flexible widget-level configuration for time, category, value, aggregation, and visual styling.
- Support for multidimensional analytical visualizations.
Advantages
- Rapid dashboard development without extensive front-end coding.
- Suitable for continuously arriving operational and sensor data.
- Supports both conventional monitoring and advanced analytical visualization.
- Enables multiple water-quality and process variables to be analysed within a unified environment.
- Suitable for industrial and process-monitoring applications.
In this project, Dashtera provides the final analytical layer of the streaming pipeline. It transforms continuously arriving treatment-plant measurements into visual information that helps operators monitor plant condition, evaluate treatment effectiveness, detect unusual events, and investigate relationships among process variables.
5. Dashboard Analysis
The Water Treatment Plant Quality and Process Monitoring Dashboard consists of three interactive pages providing complementary views of plant operation: an overall monitoring page, a treatment-process analysis page, and an alarm-focused advanced analytics page.
5.1 Water Treatment Plant Overview
The first page provides an immediate view of overall plant operation and final-water quality.
Three gauges display Average Water Quality Score, Average Flow Rate, and Average Final Water pH, while Current Plant Status and Critical Water Quality Events indicate current operating condition and serious quality deviations.
Time-series charts track Water Quality Score, Flow Rate, Final TDS, pH, Conductivity, and Turbidity. These allow operators to monitor both current values and recent changes.
The Water Treatment Process visualization represents the progression from intake through coagulation, filtration, disinfection, and final treatment. Temperature distribution charts provide additional information about operating conditions, while the Flow Rate vs. Quality Score scatter chart explores the relationship between throughput and overall water quality.
Overall, this page provides a concise operational overview of plant health and final-water performance.
5.2 Water Quality & Process Monitoring
The second page focuses on treatment effectiveness and changes in water quality throughout the process.
Five KPIs summarise Average Final Turbidity, Turbidity Reduction Efficiency, Average Final TDS, Average Conductivity, and Average Chloramines.
Multi-line charts compare pH, turbidity, and TDS across Intake, Coagulation, Filtration, Disinfection, and Final Treatment. These visualizations make it possible to identify how each treatment stage contributes to the final-water condition.
The Treatment Stage Comparison chart clearly shows progressive turbidity reduction from intake to final treatment.
Histograms and distribution charts provide statistical views of turbidity and pH, while 3D visualizations analyse relationships among pH, turbidity, TDS, conductivity, and quality score.
Overall, this page provides a deeper view of how treatment-stage performance influences final-water quality.
5.3 Water Quality, Alarms & Advanced Analytics
The third page focuses on abnormal conditions and advanced analysis.
Five KPIs monitor Total Warning Events, Total Critical Events, Maximum Turbidity, Maximum TDS, and Maximum THM.
The Water Quality Status Over Time and Water Treatment Alarm Events charts show when abnormal conditions occur. Alarm distribution and Pareto charts identify the most frequent alarm categories and their cumulative contribution.
Regression charts analyse Turbidity vs. Water Quality Score and TDS vs. Water Quality Score, while conductivity histograms and distribution curves show parameter variability.
The Water Treatment Operating Space 3D chart combines temperature, turbidity, and flow rate, helping identify normal operating clusters and unusual process conditions.
Overall, this page supports alarm prioritization, anomaly investigation, and diagnostic analysis.
6. Dashtera Integration with InfluxDB Cloud 3
InfluxDB Cloud 3 serves as the time-series backend for the project. The data pipeline begins with the prepared water-treatment CSV dataset. A Python streaming script running on an Oracle Cloud Always Free VM continuously reads the data and sends timestamped measurements to InfluxDB Cloud 3.
The streaming script is managed as a systemd service, enabling automatic startup and restart for continuous operation.
Dashtera queries the stored data for individual dashboard widgets. Aggregate queries support KPIs and gauges, while timestamped observations are used for trends, regression, distributions, and 3D visualizations.
The complete pipeline is:
CSV → Python Streaming Service → Oracle Cloud VM → InfluxDB Cloud 3 → Dashtera
This architecture provides a lightweight approach for simulating continuous industrial water-treatment telemetry and near-real-time dashboard monitoring.
7. Discussion
This project demonstrates how water-treatment data can be transformed into a continuously updating BI solution using Dashtera, InfluxDB Cloud 3, Python, and Oracle Cloud.
The three dashboard pages provide complementary views of plant operation: an overall monitoring page, a treatment-process analysis page, and an alarm-focused advanced analytics page. Together, the KPIs, gauges, trends, treatment-stage comparisons, statistical distributions, regression models, Pareto analysis, and 3D visualizations support both operational monitoring and diagnostic investigation.
The project also demonstrates how a static CSV dataset can be extended into a simulated IIoT architecture through continuous Python streaming, cloud-based time-series storage, and no-code visualization.
Although the solution represents a simulated treatment environment rather than a production control system, the same architecture could be extended to real sensors, IoT gateways, SCADA data, or other industrial water-treatment sources.
8. Conclusion
The Water Treatment Plant Quality and Process Monitoring Dashboard successfully demonstrates how streaming time-series data can be transformed into an interactive, near-real-time business intelligence platform. By combining Dashtera, InfluxDB Cloud 3, and a lightweight Oracle Cloud deployment, the solution provides continuous visibility into plant performance, water quality, treatment effectiveness, and alarm conditions.
The dashboard architecture is practical and extensible: the same pipeline pattern could be applied to real sensor networks, SCADA systems, or additional treatment facilities without structural changes to the dashboard design.
Dataset Reference
Idea Source: Water Potability — Kaggle
Streaming Dataset:
A water-treatment time-series dataset developed from the original water-potability concept and extended with treatment-stage measurements, timestamps, operational variables, quality indicators, status information, and simulated alarm events.
Infrastructure:
- Data Source: CSV
- Streaming: Python
- Cloud Compute: Oracle Cloud Always Free VM
- Process Management: systemd service
- Time-Series Database: InfluxDB Cloud 3
- Visualization: Dashtera
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