The Future of Automation Technology in Asphalt Plants


Asphalt plant operators are no longer strangers to industrial automation solutions, and the vast majority have automated several of their processes.

MINDS’ DrumTronic plant control system, pictured, offers a preview of what full automation might look like for asphalt plants.

Over the course of a more than 30-year career, Pierre Vidaillac has seen the asphalt industry increasingly turn to automation to improve efficiency and reliability. Today, he says, asphalt plant operators are no longer strangers to industrial automation solutions, and the vast majority have automated several of their processes.

“We’re at a time where it’s not possible to think of asphalt plants without automation; we’re beyond that,” says Vidaillac, the CEO of MINDS Inc., a leader in advanced customizable asphalt plant control systems. “We were initially at a very low level, the automation just covered the main functions that could not be easily done by humans, but now the automation is touching all aspects of the plant.”

But there’s still room for plant operators to automate further and more benefits that they can receive from the process. We spoke with Vidaillac about the future of asphalt plant automation, and what other technologies that automation could enable for the industry.


Where we are: current asphalt plant automation

Currently, automation is being used in almost all areas of the plant. Supervisory control and data acquisition (SCADA) software is improving mix quality by managing raw material quality and automating the proportions, mixing, adjusting for water content and temperature of the asphalt production process. Automation technology and well-designed human machine interfaces (HMI) help reduce human error, and reduce the number of variables operators need to pay attention to, Vidaillac says. Automation controls also help to prevent failures before they happen, thereby preventing unnecessary downtime.


The potential of full plant automation

Though seeing an autonomous asphalt plant may seem like the stuff of science fiction, Vidaillac says it’s not from out of reach. With automation already allowing for the minute fine-tuning of mixing, burners and heat exchange, optimization of energy consumption, fault detection, mix precision and more, partial autonomy could happen in the next few years.

Some existing asphalt plant software, such as MINDS’ DrumTronic and BatchTronic plant control systems, offers a preview of what that might look like. Both programs take a holistic approach to plant automation and offer the possibility for total plant control.

This will have major implications for plant workers, who have already been moved away from the more dangerous jobs in the plant but will now be required even less. But, Vidaillac says, the end goal of industrial automation is certainly not to eliminate the need for human operators and their judgment.

“The ideal situation is to keep both, a good and experienced operator on a highly automated system,” he says. “Sometimes, unfortunately, companies are not training their operators enough because they feel the automation can take over, which is true only to a certain extent. The training of new operators is still needed. Making asphalt is mostly a physical and chemical process that depends on a lot of mechanical parts to function. Automation can’t replace maintenance.”

Vidaillac says an ancillary benefit of full plant automation will be the use of mobile applications to deliver more relevant information right to the cell phones of plant operators.


Where we’re going: process modelling and artificial intelligence

Once asphalt plants have been fully automated, operators will begin to ramp up their use of analytics to establish and meet key performance indicators. “Analytics can give plant operators information that was not easily observable before,” he says.

Vidaillac says he also expects to see plants begin what he calls industrial process modelling.

“We model the process and the computer knows what to expect, it has a full mathematical model of the plant,” he says. Sensors input throughout the plant are compared to model output and any deviation from how the plant should be performing according to the model will raise an alarm for operators.

From there, plant operators can choose to trust the sensors, trust the model or tweak the model itself through machine learning.

After operators implement plant modelling, artificial intelligence won’t be far behind. Vidaillac expects AI solutions will use the existing industrial automation controls to learn from the plant’s history and make changes to improve its performance.

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