Looking for ways to improve the efficiency of its power plant operations while reducing costs and environmental emissions, the Department of Power and Water at Michigan State University (MSU) began a study using multivariate data analytics that led to some surprising findings. The results have implications that could help other operators of large-scale power facilities reduce their carbon footprint and improve power plant operations.
Finding the right balance between efficient power output from boilers and other energy producing equipment while also reducing environmental emissions is an important objective for power plant operators. Governments and environmental agencies around the world establish emission standards as part of air pollution regulations, but finding the right way to meet the standards can vary greatly depending on location, equipment and other operating factors.
In agrochemical, pharmaceutical and other industries that manufacture complex chemicals, finding ways to reduce waste and improve inefficiencies often hinges on selecting the right chemical compounds. Data analytics can help manufacturers find alternative compounds that meet complex requirements, decrease raw material usage or enable more cost-effective, sustainable processes.
The potential for Artificial Intelligence (AI) is enormous and the applications seemingly unlimited. One subset of AI, deep learning, offers the promise of efficiently solving a large range of challenges involving unstructured data by harnessing neural networks to save time and money, and even perform seemingly impossible tasks.
Deep learning has revolutionized the fields of artificial intelligence, computer vision, speech recognition, and more in recent years. Deep learning can draw information from unstructured data such as images or text in a way that was unthinkable a decade ago. In industries like Pharma and Biopharma, deep learning can help all the way from understanding how cells work using live cell imaging to monitoring manufacturing using audio.
What’s the secret formula for creating long-lasting bubbles? Is expert knowledge of liquid dynamics needed to optimize the mixture design and develop the best bubble solution? Or can we use design of experiments (DOE) and data analytics to draw conclusions? Let’s a take a look at a fun example of how DOE can be used to optimize a mixture design in order to achieve our goal: create long-lasting bubbles.
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.
Multivariate data analysis (MVDA) is a statistical technique that can be used to analyze data with more than one variable in order to look for deviations and understand the relationships between the different data points. In practice, this can mean taking data from a number of different sources and turning it into meaningful information from which you can draw some conclusions.
At the heart of any process used to manufacture biological products is a bioreactor setup that supports a stable and reproducible biologically active environment. The bioreactor provides a controlled environment to achieve optimal growth for the particular cell cultures being used.
Worldwide demand for energy escalates every year, and the consumption of fossil fuels continues to increase despite the growing supply of alternative energy options. Globally, about 81 percent of energy comes from a finite supply of fossil fuels like oil, coal and natural gas. Fossil fuels are used to heat homes, run vehicles, power industry and manufacturing, and provide electricity.
All manufacturing industries need good control and good overview of their production processes. As already discussed in a previous blog post, SIMCA-online enables you to apply advanced multivariate data analytics in real time to monitor your production processes, for example to make sure that your production process is behaving as it should or that the quality is what it should be.
An important environmental issue that has come into focus is the increasing number of chemicals that we are exposed to in our everyday life. Chemicals are found in products ranging from cars and furniture to clothing and skincare, and are also by-products from combustion. The CAS REGISTRYSM, an international standard for chemical information, currently contains more than 134 million unique organic and inorganic chemical substances and more than 67 million sequences.
If data analytics were easy, everyone would do it, right? Well, what if were easy enough for anyone to do it? Can you image what sort insights you might glean from the vast pools of data your company collects about your manufacturing processes, sales or production outputs?
If all of your data stays hidden in the depths of some process control computer or in Excel spreadsheets on the manufacturing floor manager’s desk, are they doing anyone any good?