Sucro Sense (Predicting the Normal Recovery of a Sugar Depending Upon Brix, Polarity and Temperature)

Abstract

The sugar industry forms a vital segment of agricultural economies, where profitability is directly linked to the efficiency of sugar recovery from sugarcane. Accurate estimation of normal recovery is essential, which depends on key parameters such as Brix value, Polarity, temperature, purity, Java ratio, and total loss. Conventional approaches involve manual operations, such as temperature corrections using lookup tables and subsequent formula-based calculations. These traditional methods are often time-intensive, susceptible to human mistakes, and result in inconsistent data handling, ultimately reducing operational efficiency. To overcome these drawbacks, this project introduces Sucro Sense, a software-driven solution designed to automate the estimation of recovery. The system accepts inputs such as observed Brix, temperature, and Polarity, then employs correction algorithms and validated formulas to derive corrected Brix, purity, Java ratio, losses, and expected recovery. Additionally, the implementation of machine learning, particularly Random Forest Regression, enhances prediction accuracy up to 86 percentage. The tool provides outputs in both tabular and graphical formats, ensuring clarity for end-users. By digitizing workflows, Sucro Sense reduces manual dependency, minimizes computational errors, and improves productivity. Ultimately, this project streamlines sugar processing operations, delivering a reliable, efficient, and scalable approach for precise sugar recovery estimation in industrial contexts.

KEYWORDS: Sugar recovery, Brix value, Polarity, Temperature correction, Purity, Java ratio, Total loss, Automation, Machine learning, Random Forest Regression, Software solution, Agricultural processing, Sugarcane industry, Error reduction, Productivity enhancement.