Optimizing the function of boilers, turbines and other capital equipment used to generate power requires a careful balance of fuel, heat, pressure, operator proficiency and many other variables. Managing the process on a day-to-day, or minute-to-minute basis, is like performing a skilled and well-orchestrated dance—partly based on data, but also based on operator expertise. Yet, adding more accurate information to the equation can potentially save millions of dollars, cut emissions significantly and even expand the working life of your equipment.
Over the last several years, the use of artificial intelligence (AI) in the pharma and biomedical industry has gone from science fiction to science fact. Increasingly, pharma and biotech companies are adopting more efficient, automated processes that incorporate data-driven decisions and use predictive analytics tools. The next evolution of this approach to advanced data analytics incorporates artificial intelligence and machine learning.
The key to being able to innovate, improve and streamline your processes often lies in gaining as many insights as you can from a variety data sources scattered throughout your operations. Making sense of all that data can be difficult. But it's not an impossible dream.
Could data analytics aid in the diagnosis of severe neurological diseases? In a recent study, a research group at Umeå University has conducted statistical data analysis of biomarkers from patients suffering from Amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig’s disease) and Parkinson’s disease to investigate whether data analytics could help in the diagnosis of – and help distinguish between – the two diseases.
Mining information in unstructured text can be a real challenge. Patent documents, for example, provide a rich source of technological and scientific knowledge that can reveal technological trends as well as information on the legal landscape of the market. This makes analysis of the vast and ever-growing number of patents an important part of corporate business strategies.
On the west coast of southern Sweden, facing the expanse of the ocean, is the beautiful city of Gothenburg. Surrounded by a string of islands, this city has been the home for sailors and merchants, seafaring and shipping, since ancient times. One of the islands to the north of Gothenburg is the picturesque island of Tjörn. Once every year, Tjörn is the location for one of the most famous sailing races in Sweden – “Tjörn Runt” or “Around Tjörn”.
Biopharmaceutical companies today are challenged to develop high producing cell lines as quickly as possible. Commercially available media may fall short of performance expectations required to meet targets. The alternative —fully customized media and feed development — requires significant funding, time and in-house expertise in media development.
In life science, biopharma and other areas of research, development and production, design of experiments (DOE) provides a systematic method to determine cause and effect relationships between factors and responses affecting a process, product or analytical system. But the key to understanding your results is effective analysis of your experimental data.
Principal component analysis, or PCA, is a statistical procedure that allows you to summarize the information content in large data tables by means of a smaller set of “summary indices” that can be more easily visualized and analyzed. The underlying data can be measurements describing properties of production samples, chemical compounds or reactions, process time points of a continuous process, batches from a batch process, biological individuals or trials of a DOE-protocol, for example.
How Multivariate Data Analysis Can Separate the Players from the Gorillas (MVDA for beginners)
We have more data than ever before coming at us from many sources – both in our personal lives as well as business. Data is everywhere: from the production flow of a manufacturing floor to the sales results in a grocery store to the number of shares a page gets on Facebook. How do you sort it all out in a way that makes sense? Which data should you worry about and which should you ignore?