Machine learning is becoming increasingly important in the field of chemical engineering. Its applications have the potential to revolutionize various aspects of chemical engineering, research, and industrial processes. This interesting topic came to us from in their article, “Research team envisions a bright future with active machine learning in chemical engineering.”

Machine learning algorithms can optimize complex chemical processes by analyzing data in real-time. This can lead to increased efficiency, reduced energy consumption, and cost savings.

Chemical engineering generates vast amounts of data. Machine learning in combination with the latest sensor technology captures the essence of the greatest portion of this data, exposing patterns, trends, and anomalies that would otherwise be hidden to traditional methods.

As the technology continues to advance and more data becomes available, machine learning’s importance to this field is expected to grow even further, making it an essential skill for modern chemical engineers.

The biggest challenge is that most organizations have little knowledge regarding how artificial intelligence (AI) systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.