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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

204
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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Quality of Water01:19

Quality of Water

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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
224

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Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring.

Akashdeep Bhardwaj1, Vishal Dagar2, Muhammad Owais Khan3

  • 1School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.

Environmental Science and Pollution Research International
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This research introduces an Internet of Things (IoT)-based framework for real-time water quality management. The system efficiently monitors water parameters and device performance, offering a proactive solution to the global water crisis.

Keywords:
AIEmbeddedIoTMicrocontrollerReal-timeSensorWater quality monitoringWireless

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Sensor Technology

Background:

  • Increasing global population and rising temperatures exacerbate water scarcity, threatening human health and ecosystems.
  • Traditional water quality and device management are resource-intensive, requiring significant manual effort and investment.
  • Mismanagement of water resources poses serious risks to public health and environmental sustainability.

Purpose of the Study:

  • To develop an Internet of Things (IoT)-based real-time framework for proactive water quality management.
  • To integrate machine learning for analyzing water quality trends and device performance.
  • To provide an efficient system for monitoring, alerting, and managing water parameters and smart devices.

Main Methods:

  • Implementation of an IoT-based framework for real-time data acquisition from water quality sensors and smart devices.
  • Utilization of machine learning models to analyze water quality trends and predict potential issues.
  • Development of a monitoring and alerting system based on contamination and toxic parameter levels.

Main Results:

  • The proposed IoT framework enables efficient real-time water quality monitoring and management.
  • Machine learning models effectively analyze water quality trends and device performance data.
  • The system demonstrates superior efficiency in managing and accessing water parameters compared to existing methods.

Conclusions:

  • The developed IoT framework offers a significant advancement in water quality management and smart device monitoring.
  • Real-time data analysis and proactive alerting are crucial for addressing water scarcity and ensuring water safety.
  • This approach provides a scalable and efficient solution for optimizing water resource utilization and protecting public health.