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In the era of Industry 4.0, manufacturers such as Dow are focused on gaining more value from their historical data than ever before. ProSensus has distinguished itself as a trusted, global leader in Big Data analytics by helping many Fortune 500 companies across numerous industries use their data to innovate for the future.

ProSensus has recently worked with Dow to accelerate innovation on one of their key product lines – silicone antifoams. For product development applications such as this, ProSensus combines powerful multi-block latent variable modeling with constrained optimization. This approach allows clients to simultaneously optimize the selection of raw materials, recipe formulations, manufacturing conditions and costs to reach targeted product performance properties while adhering to custom constraints. A model-based approach allows Dow to rapidly develop and scale up custom silicone antifoam formulations for the dynamic technical and regulatory needs of customers in the pulp and paper, food and beverage, wastewater treatment, metal working, and other industries.

This presentation will examine the approach taken (data assembly, multivariate modeling, model validation, and optimization), challenges encountered, and results obtained. Perspectives from both ProSensus and Dow will be included.