AI In Industrial Processing: Applications And Challenges

What is Artificial Intelligence?

In general, AI is a large field of study that aims to create machines and systems that mimic human intelligence such as reasoning, understanding language, learning, and problem solving. Inside that field of study are subfields such as machine learning, robotics, computer vision, and more. Therefore, AI is a broad term, encompassing much.

For example, a program that labels whether the animal in a picture is a cat or a dog, is considered AI. Additionally, a lawn mower that navigates and cuts your lawn autonomously, also belongs to AI. Both examples show programs or machines displaying a form of human intelligence (recognizing a cat or dog from a picture, and navigating a lawn), however their complexities differ.

Likewise, AI capabilities differ in complexity, although their development and functions are similar. Namely, the programs need to be trained on lots of data before they can be used.

Industrial AI

Now that we have touched lightly on what AI is and discussed some functions, the next question is how can AI improve industrial project and turnaround management processes and enable industry professionals to enhance their abilities and productivity? This is exactly what “industrial AI” is concerned with.

What is Industrial AI?

Industrial AI aims to leverage technologies to improve efficiency and product quality via automation.

Key applications of AI for the industry

The following are examples of applications, to give an idea of what can be gained from implementing AI in the industry:

1. Forecasting project delays: These days, a lot of data is being collected, also when planning and executing projects. AI could analyze historical project data and compare this to ongoing project metrics to forecast potential outcomes and delays. This allows project managers to act in advance and avoid disruptions.
2. Autonomous routine operations: Machines, computers and drones equipped with AI can perform routine operations autonomously, reducing the need for manual field checks, thereby improving efficiency. This is true for plant and office environments. People can spend more time on analysis and control, and less time on bulk work.
3. Speeding up estimating processes: AI can be trained to interpret piping and instrumentation diagrams (P&IDs), automatically create material takeoffs (MTO) and generate estimates based on the wealth of data from past projects, speeding up the estimating process and reducing mistakes.
4. Optimizing maintenance schedules: AI can assist in generating optimized maintenance schedules based on resource availability and equipment performance, leveraging data and real-time input from the field.

Overall, there are many functions and tasks that can be automized and improved by leveraging industrial AI. However, as promising as that sounds, there are also some challenges when implementing AI in the process industry.

Challenges in adopting AI

Although AI has exciting potential, there are challenges to its implementation.

1. The need for high-quality data: AI relies heavily on large amounts of high-quality data. If the data is incomplete or inaccurate, the AI’s performance can suffer, leading to unreliable results. Look for tooling that has access to large amounts of data and prepare your business by starting to collect and structure (more) data.
2. Complexity and cost: Developing AI systems is complex and expensive. These systems require vast amounts of data and specialized expertise, which can make adoptions challenging for many organizations. Technology providers are investing in AI solutions, but it is still unclear what can be achieved, what the exact benefits and the associated costs are.
3. Resistance to change: Professionals may be hesitant to trust and adopt AI systems. Change can be difficult and there is often a fear of being “replaced” by automation, leading to resistance in the workforce. Workers need to be educated. The goal is to evolve ourselves to spend our time on new things and bring the business to the next stage.
4. Data security risks: Most AI solutions come from external vendors, which can introduce data security concerns. Granting access to confidential project data increases the risk of data breaches and unauthorized use. Data security certification and auditing is more important than ever.

Final thoughts on AI in Industrial Processing

As AI continues to evolve, its potential applications in industrial processes are becoming increasingly apparent. For project managers, estimators, planners, and other professionals, AI holds tremendous potential to improve efficiency, streamline operations, and optimize decision-making. From predictive project management to automated routine tasks, the technology offers significant opportunities to enhance performance and reduce manual workloads.

However, the road to successful AI implementation is not without its challenges. Ensuring high-quality data, managing development costs, and addressing concerns over job displacement and data security are crucial to unlocking AI’s full potential. By understanding both the possibilities and limitations of AI, professionals in industrial processing can better prepare themselves for its integration and leverage it to drive innovation and productivity.

This blog is just the beginning of a deeper exploration into (industrial) AI. As you continue following this series, you will gain a more comprehensive understanding of how AI can transform your role and the way the industry might evolve, equipping you with the knowledge to make informed decisions about its application. The future of AI in the industry is bright, and staying ahead of these technological advancements is key to ensuring long-term success.