Process parameters optimization using machine learning

Machine learning in process parameters optimization
Glass Manufacturing with applied machine learning

Optimization of process parameters using machine learning improves efficiency even in such a well-established industry as manufacturing. This data-driven approach allows us to find complex, non-linear patterns in data, and transform them into models, which are then applied to fine-tuning process parameters.

Traditional control systems rely on a rule-based scheme, expertise, and domain knowledge of particular technologists. Today’s modern manufacturing facilities are becoming more and more complex with interlinked processes, and we are rapidly reaching the limit of our capacity to include every aspect of the process in a rule-based expertise-based model. Machine learning offers an extremely effective solution that overcomes the challenge presented by increasingly complex processes. BitRefine builds models that “keep in mind” 1000-dimensional spaces of interlinked parameters, and that are capable of finding their optimal combination.

Who can benefit from applying ML?

There’re thousands of types of industrial processes that can benefit from applying machine learning:

  • Chemical industry
  • Plastics industry
  • Glass manufacturing
  • Semiconductor manufacturing
  • Ore beneficiation
  • Gas processing
  • Oil refinery

Optimizing production process with BitRefine Group

Over next decade, artificial intelligence and machine learning will drive innovation in the manufacturing sector. To the moment, the industry has already established IT environment, generating and storing great amounts of diverse data from sensors on a production line, environmental data, and machine tool parameters. Sufficient volume of data allows us to build a reliable data-based model and increase insights of the process properties. Being a pure machine learning company, BitRefine group helps clients to start pulling a real profit from accumulated data.

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