Leading Chemical Manufacturer - Customer Case Study

To optimize PVC production by precisely predicting the cooling tower's water temperature, we analyzed sensory and weather data from 2015 to 2021, utilizing regression models to understand the factors affecting cooling capacity. This analysis led to a 2-8% increase in PVC production across various lines, employing three models: an updated Excel curve, a linear regression model considering weather parameters, and the most effective LightGBM model incorporating 80 different inputs, including historical data. The implementation of these models significantly improved the accuracy of temperature forecasts, reducing the average error from 1.90 to 0.51, thereby maximizing production efficiency.