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CAMO Software - Multivariate Analysis and Design of Experiments Software White Papers

CAMO Software  - Multivariate Analysis and Design of Experiments Software

Generate Clean Energy More Efficiently With Powerful Data Analytics Advanced data analysis tools can help renewable energy companies operate more efficiently, from R&D - such as developing more efficient solar panels and more robust wind turbine blades - to solving common problems such as predicting turbine breakdowns and optimising the use of equipment depending on local conditions and demand.
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Real-Time Process Monitoring If you are running a manufacturing of industrial process, you’ll appreciate the value of being able to identify and correct deviations before they become a major problem. Now you can have real-time process monitoring using powerful multivariate analysis, without the inflated price tag or complex installations of other solutions
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Optimize Refining Processes & Reduce Costs From Distillation to Blending CAMO Software has worked with petrochemical refineries for many years. Their world-leading multivariate data analysis software can be used for process modelling, rapid analysis and early event detection.
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Combining First Principle Models with MVA and DOE Chemical engineering is a discipline that to a certain degree relies on deterministic or first principle models. These models may describe flow rates, mass balance, energy and other principal properties of a system. But to quote the famous chemist/statistician George Box: “All models are wrong but some are useful”.
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Multivariate Data Analysis an Essential Tool in the World of Big Data Multivariate data analysis (MVA) is increasingly used across a wide range of industries from R&D and engineering, to manufacturing, quality control and preventative maintenance programs. Below are some examples of how our clients are using multivariate data analysis to get a competitive edge.
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CAMO Delves into Assumption Free Modeling and Monitoring of Batch Processes Modeling strategies currently in use for the monitoring of batch processes wheremultivariate data are available have some limitations, particularly for batches where the true starting or end point are not the same on an absolute time scale, or the batch progression varies among batches.
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