By Ryan Ayers
The field of chemical engineering has seen substantial growth thanks to technological advances and data utilization. Innovation has surged forth thanks to big data’s insight and integration. Through big data interpretation, chemical engineers have been able to invent new processes, increase efficiency, expand volume, and cooperate closely with other industries.
An introduction to big data
Instead of looking at big data as a thing, it’s more accurate to think of it as a process. Although much of this concept depends on a massive collection of data, it’s the analyzing and interpreting of this data that makes big data valuable, particularly for chemical engineers. For chemical engineers looking to take full advantage of big data, it’s essential to consider the questions one is trying to solve with their experiment. Through this questioning, engineers will be able to uncover patterns and trends within large amounts of data. These patterns and trends can provide new avenues of exploration, expanded knowledge and deeper insights, making big data a key to future success in any industry — including chemical engineering.
Big data’s benefits to chemical engineering
Although early, big data’s incorporation into chemical engineering has already yielded multiple benefits and helped a number of chemical engineering organizations gain a competitive advantage. For example, expanded data volume has led to a greater comprehension of issues and more rapid realization of solutions. Additionally, time and money have been saved due to big data’s organization of data.
Chemical engineers are also discovering more experimentation options; basically, big data lends itself to theoretical experimentation which is safer and faster than traditional methods. Another interesting benefit has been the merging of industries. Communication between chemical engineers and other professionals in related fields, like computer science and medicine, has been improved thanks to the connectivity big data provides. Now, information is being shared in real-time for increased productivity.
How chemical engineers can leverage big data
Data itself is useless without a direction or intention. The goal is to find the appropriate data relating to the experiment and predetermined focuses. Chemical engineers don’t need every bit of potential that big data can offer, so they utilize specific branches. One such branch, called cheminformatics, can be utilized by chemists to create new chemicals and materials. But how? Cheminformatics provides comprehensive data and computational tools capable of generating toxicity models, model microorganism pathways, and discover renewable chemical alternatives.
Understanding big data is still a challenge, regardless of industry. Because it is a tool and process, big data tends to adapt to the user’s understanding of interpretation. As a result, some individuals may see nothing more than a mere collection of data without giving much thought to how it’s processed or how it can be made useful.
Chemical engineers have to remain focused on the questions they ask of collected data, or else they may find inaccurate results. Wading through the amount of data is difficult, so chemical engineers may need to include data analytics within their training and studies.
The future: more innovation in less time
Big data is comprised of three V’s: volume (amount of data), velocity (rate of data generation), and variety (data source growth). The three V’s of big data organically lead to the acceleration of innovation. More data and interpretation equals more growth, which then ultimately leads to even more useful data. That’s why an industry like chemical engineering is witnessing such expansive growth during our current data explosion: previous practices and methods restricted by old technologies have given way to real-time results, communication, and experimentation. Look for even more innovation in less time as the merger between big data and chemical engineering continues to mature.