AI Case Study

Newcrest Mining prevents mill downtime at its Lihir mine using machine learning to predict when an overload is about to occur

Newcrest Mining has worked with PETRA at its Lihir mine to develop algorithms using supervised machine learning which provides warning of increasing probability that the mine will experience an overload before it happens. This prevents SAG mill downtime at the mine and since implementation Lihir had not experienced any downtime at the time of writing.

Industry

Basic Materials

Mining And Metals

Project Overview

"This led to further work at Lihir. The mine operates three SAG mills and, in October 2016, Newcrest engaged PETRA to develop overload algorithms for each of the machines. There are leading indicators as to what’s going to happen, and an algorithm is a mathematical equation that describes all of those signals. It either feeds that information back into the mine’s automated process control system or, in this case, it triggers a visual warning so that the operators can respond to the increasing probability of an overload event. At Lihir, the algorithm predicts the probability of an overload event in an hour’s time, which allows the mine to take appropriate action to prevent it."

Reported Results

According to Newcrest Mining: "preventing one or two downtimes paid off the cost of the project... Lihir was having multiple overload events each year. And since implementing the algorithms in January this year, they haven’t experienced any overloads.”

Technology

"The algorithms effectively allow the mills to operate at maximum throughput with minimum risk of overload. The main challenge was the quantity of data based on failures compared to normal operating conditions, which made it difficult to predict when these events were going to happen, particularly an hour in advance... Often, machine learning specialists use unsupervised machine learning in this type of scenario, but in this case, that wouldn’t give the mine enough time to respond. That’s the advantage of using a supervised machine learning algorithm.”

"...engineering knowledge was used to create an additional 650 engineered signals from the original 143 raw signals for each SAG mill (about 800 signals per mill). Including engineered signals in the algorithm development significantly increased the accuracy of the algorithms."

Function

Operations

General Operations

Background

"Located on Aniolam Island in the New Ireland Province of Papua New Guinea, the gold deposit at Lihir is one of the largest in the world; since starting production in 1997, the mine has produced more than 10Moz of gold. Most of the ore is refractory and is treated using pressure oxidation before the gold is recovered by a conventional leach process. The mine operates three semi-autogenous grinding (SAG) mills and, in October 2016, Newcrest engaged PETRA to develop overload algorithms for each of the machines. 'When a SAG mill overloads and becomes too full, it causes the mill to trip and stop,' Stewart explained. 'Sometimes it can take quite a while to get it running again. Because the SAG mills at Lihir are critical pieces of production equipment, and there’s no way of diverting material, it was really important to avoid these events wherever possible.' Indeed, every minute of downtime equals lost production, and the power draw (and cost) required to restart a SAG mill is significant."

Benefits

Data

Historic data over the course of a year resulting in more than 360 million lines of data over 130 variables: "There is a huge amount of data that comes off a SAG mill – noise is recorded, power, speed, energy consumption and control parameters – there are hundreds of measurements, usually taken at five-second intervals that need to be analysed."