One of the main concerns of manufacturers is how to improve productivity. This includes analyzing, measuring, modeling, and implementing specific actions to optimize their production lines.
The reason why production optimization plays a huge role in manufacturing industry is that it is the core means to increase revenue and reduce costs. Failure in the production process results in the loss of quality and production – which directly translates into loss of revenue.
Let’s look at the chemical manufacturing industry, because the birth of heavy chemical industry coincides with the beginning of the industrial revolution. The chemical industry accounts for about 15% of U.S. manufacturing, produces more than 70000 different products, and is responsible for 90% of our daily products.
Challenges for chemical manufacturer
Just like the chemical manufacturing industry, it faces a wide range of process optimization challenges.
In order to optimize the production line of chemical manufacturers, they need to solve the low efficiency problems of different processes, such as the formation of undesirable by-products, process instability, loss caused by impurities, and so on.
Given the complexity of chemical manufacturing, it is time-consuming and difficult to understand the root causes of these inefficiencies, let alone predict when they will occur. Usually, it is the specific behavior of multiple production parameters or tag combinations that leads to inefficiency.
More and more chemical manufacturers are turning to industrial artificial intelligence solutions, using supervised and unsupervised machine learning methods to identify and predict process inefficiencies.
According to a recent study by Accenture, companies implementing industrial artificial intelligence in the chemical industry are seeing huge benefits – up to 72% of companies report that some process KPIs have at least tripled, and 37% have increased five times. For example, a dichloroethane manufacturer implemented process based industrial artificial intelligence to address a range of process inefficiencies and, by doing so, increased production by 1.7 million euros in less than 12 months.
With the capabilities provided by industrial AI, chemical manufacturers can use their data to improve their processes and constantly adjust them.
By using process based machine learning, manufacturers can obtain focused and context sensitive predictive alerts. This is a huge opportunity for chemical manufacturers, because operational technology (OT) data has been well organized and captured by data historians.
Using these data and process based AI means that the root causes of process disturbances can be identified with great accuracy, and process instability and failures can be predicted before they have a chance to affect production.
Therefore, with industrial artificial intelligence, chemical manufacturers can reduce quality and production losses and save a lot of time and money.