Real cases

Industrial artificial intelligence for cane sugar refineries

A cane sugar refinery is one of the best-automated plants in the food industry and, at the same time, one that still moves a remarkable amount of paper. The control system runs the refining train with precision — affination, clarification, decolorization, crystallization, drying — yet the shift is still run on a handwritten log, the lab on a spreadsheet in a shared folder, and raw sugar receiving on a weighbridge ticket somebody types twice. The process is world class; the business runs behind it.

See the 12 cases, one by one ↓

That gap has a concrete and always identical cost: final quality is decided upstream — in incoming raw sugar quality, in activated carbon dosing, in the boiling curve — but measured downstream, in the lab, hours later. By the time the analysis says color, moisture or grain size has drifted out of range, there are tonnes already made. And in a product shipped in bulk to bottling, baking and confectionery plants, an off-spec lot is not just another defective product: it is a customer's line stopped, and an account at risk.

On these pages you will find twelve IRIS system use cases applied specifically to cane sugar refining, grouped into the three blocks of the iLEAN architecture: Connect (capturing what is lost today in papers, panels, emails and spreadsheets), Edge (computer vision on the bagging line itself and at product changeovers) and Agents (audit evidence that builds itself, and the complete system coordinated against the off-spec lot). Each case includes the real problem, how the architecture fits, and an estimated return in conservative ranges.