Developing Process Control Strategies for Continuous Bioprocesses
Process control enables biomanufacturers to ensure that operating parameters are within defined specifications. A control strategy should be established during early stages of process development…
Motiva moves analytics beyond Microsoft Excel
Just as input from humans can add context to data and allow better decisions, it can also enable analytics to diversify, and make itself useful in a greater variety of applications.
Solving Bulk Solids Problems with Advanced Analytics
This article highlights pain points in common industry operations related to solids handling and identifies opportunities for the application of SME-driven advanced analytics to generate…
Harnessing machine learning - part two
In the second part of this two-part article, Lisa J. Graham, Seeq Corp., provides use cases of machine learning in action in the oil and gas industry.
Harnessing machine learning - part one
Machine learning (ML) is a powerful tool, but it requires extensive domain expertise for successful deployment, as Lisa J. Graham, Seeq Corp., explains in the first part of a two-part article.
Finding the silver lining in cloud analytics
The questions you ask to uncover your problems should inform your reliance on the cloud.
Using Online Transition Analysis to Predict Chromatography Column Failure
Advanced analytics and modeling can be used to predict downstream failures, allowing for corrective action before batches are lost.
The quest for the most magical algorithm
To create a dataset separate from its context is to create a disconnected perspective representing a static view of a constantly changing process. A smarter approach is ensuring the freedom to…
The future of manufacturing
The aluminium industry continues to make significant strides throughout its supply chain to incorporate emerging digital technologies – including those under the Industry 4.0 and Industrial…
Advanced analytics drive IIoT success
Refineries must mitigate risk, anticipate maintenance, optimize operations and minimize operational expenses. To achieve these goals, plant personnel rely heavily on data to drive decisions.