Société Le Nickel (SLN), part of the Eramet Group, has managed to improve uptime at its nickel furnace in New Caledonia by leveraging a new control system from Rockwell Automation.
Processing ores is a complex activity requiring the stable control of the rotary furnaces’ temperature profile and automating operations across different operating ranges. Feed ore introduced into the rotary kiln undergoes calcination as it travels through the length of the furnace. If the calcined product is not at a high enough temperature, product quality is compromised.
Heat is distributed across the furnace with air being supplied for combustion to occur. If there’s too much air supplied, more fuel needs to be burned to maintain the same product temperature, thus decreasing energy efficiency. Excess oxygen must be minimised to a safe level to reduce operating costs and greenhouse gases.
SLN was using an expert system, an existing fuzzy logic controller, designed to automate the operation of the furnace. However, the company faced several challenges with the legacy system, particularly with varying ore content and variable heating values leading to temperature spikes and frequent electrical trips, Rockwell explained. Trips were being caused by high product temperature, which compromises both the quality of the product, and the integrity of the equipment.
“The fuzzy logic was unable to reduce fuel fast enough to prevent trips from occurring and when in manual operation, the operators were not able to react quickly enough,” Leslie Hii, one of Rockwell Automation’s Advanced Process Control Engineers responsible for delivering the SLN project.
“Maintaining the required furnace temperature can be complex and challenging given the number of variables that need to be managed. Fuel type can be oil, coal, or a mixture of the two – each with their unique thermal characteristics. Moreover, the rate of feeding material impacts the furnace temperature and needs to be carefully managed.”
The expert system can only be turned on when the furnace is operating normally and, in the event of any instability, the operators turn it off and take control. The expert system also rated poorly in terms of user friendliness and ease of maintenance, both factors contributing to low system uptime, Rockwell said.
To rectify the situation, SLN upgraded to Rockwell Automation’s FactoryTalk® Analytics™ Pavilion8®. This model predictive control (MPC) solution offers an intelligence layer that sits on top of automation systems and continuously assesses current and predicted operational data, according to the company. It then compares this data with desired results, and drives new control targets to reduce process variability, improve performance and boost efficiency – all autonomously and in real time.
“Using a MPC solution is an ideal example of how Rockwell Automation is using artificial intelligence to drive better operational results by making use of available data,” Hii said. “In this project, we also used machine learning, process knowledge and data to develop kiln models tailored to SLN’s operations.”
The initial phase of the enhanced solution was successfully completed in just 13 months in contrast to the years it took to implement the expert system. It has already been implemented in five rotary furnaces at SLN’s facility. Operators now have the option to minimise the usage of high value fuel oil while maximising lower value pulverised coal during mixed mode operation, Rockwell said.
Mickael Montarello, Process Control Manager, SLN, said: “The MPC application can handle significant variability on ore feed and heating values and prevents trips from occurring, allowing the furnace to stay in operation at a higher rate. The calcined product temperature error was reduced by 6% and the furnace temperature profile variability reduced by 16.1%.
“The average uptime of Rockwell’s MPC is 83% compared with 70% with the earlier expert system. The new solution allows the furnace to stay in operation for longer.”
He added: “Users appreciate the tool’s user-friendliness and flexibility. In the event of a problem with one element of the process, operators can easily intervene on the element in question, while allowing the MPC to continue controlling the other manipulated variables.
“Thanks to this tool, new opportunities for optimising control and management are opening up, which were not possible with the old fuzzy logic controller. Our target for 2024 is to achieve a 90% utilisation rate.”
