Case Study

Real-time leak detection for a pipeline

How artificial intelligence and process knowledge helped develop a competitive and efficient system for monitoring small pipes

Mining

From crushing to transport, solutions with great experience of the processes

Project Data

COVERAGE

8 km

monitored pipe

Monitoring and detection for each existing stretch

DEADLINE

4 months

from project to delivery

Setup, model calibration and commissioning

SECURITY

95%

assertiveness

System tested and validated with real leaks

Context and Challenges

A large mining company had the need to monitor all the piping that transported the ore tailings, from the underflow lines of the thickeners to the dam, totaling approximately 8 km of pipeline. ​

A team of data scientists, data and software engineers, process and piping experts was assembled to deal with the following challenges:

  1. Find a means of monitoring and detecting leakage in all 6 sections of the pipeline
  2. Avoid the installation of high cost sensors or other resources that would require large investment
  3. Obtain a low number of false positives, so as not to impair productivity

Solutions Used and Equipment Provided

A system was developed that, using real-time data from existing sensors along the pipeline, makes use of machine learning techniques to model and predict leaks.

The techniques used are known as anolamy detection and, by means of energy balance equations and regressor models, allowed the system to learn the normal behavior of the pipes and any abnormal behavior to be treated as a possible leak.

The system was tested and validated with real leaks, detecting leaks of the order of 70 m³/h in a pipeline that had a nominal flow rate of 1500 m³/h with an assertiveness of 95%.

The rate of false positives was about one per week, or four per month.

Experts

Process Control Engineer

Arthur Parreira

Full Stack Data Scientist

Pablo Drumond

Head of Data Science & AI

Eduardo Magalhães

Whitepapers

whitepaper

January 25, 2021

LEAK DETECTION SYSTEM USING MACHINE LEARNING TECHNIQUES*

This article presents a leakage detection system in a slurry pipeline using a combination of machine learning techniques.

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Real-time leak detection for a pipeline

How artificial intelligence and process knowledge helped develop a competitive and efficient system for monitoring small pipes

February 2, 2021

published by

Process Control Engineer

Arthur Parreira

published by

Full Stack Data Scientist

Pablo Drumond

published by

Head of Data Science & AI

Eduardo Magalhães

COVERAGE

8 km

monitored pipe

Monitoring and detection for each existing stretch

DEADLINE

4 months

from project to delivery

Setup, model calibration and commissioning

SECURITY

95%

assertiveness

System tested and validated with real leaks

A large mining company had the need to monitor all the piping that transported the ore tailings, from the underflow lines of the thickeners to the dam, totaling approximately 8 km of pipeline. ​

A team of data scientists, data and software engineers, process and piping experts was assembled to deal with the following challenges:

  1. Find a means of monitoring and detecting leakage in all 6 sections of the pipeline
  2. Avoid the installation of high cost sensors or other resources that would require large investment
  3. Obtain a low number of false positives, so as not to impair productivity

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