Case Study:
Manufacturing Optimization
The Problem
The production of wax requires the distillation of petroleum to separate out desired distillates from waste. Measuring this before and after the manufacturing process you are able to understand how much waste was added due to the processing. The client sought an AI solution to reduce the amount of waste being added during the process and thereby increasing the amount of valuable distillates being retained.
The Solution
A machine learning XGBoost model was utilized to predict waste from hourly data collected over the past year using a wide variety of system control settings and outcome measures.
An iterative set of simulations were then run to determine the optimal control settings that result in the minimization of waste.
For more information on this AI journey, check out this brief video.
“We worked with two AI companies to optimize the crude tower at one of our plants. The success we achieved with Lityx and the LityxIQ platform was far and above that of the competitor. The LityxIQ platform outperformed the competition with greatly reduced time to analysis and at a lower cost. We look forward to a long-term partnership with Lityx and the LityxIQ platform.”
— CIO, Leading North American Chemical Manufacturer
The Outcome
77% Reduction in Waste
Why LityxIQ
Lityx is the first to make available autoML with Mathematical Optimization in our AI platform. With no coding, required, data scientists and non-data scientists have access to Accelerated, Actionable Business Solutions that are Transparent and can be Trusted.
Additional Solutions Available:
Customer Retention and Journey Analysis
Sales Forecasting
Insights Dashboard Automation
Offer Optimization
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