Real World Case Studies

How a ship-to-shore crane operation utilized Process Automated Predictive Maintenance (APdM) to significantly reduce maintenance costs, extend equipment lifespan, and enhance operational safety and efficiency.

Manufacture food and beverages with SORBA-ML. Avoid shutdowns and costly halts to production. Get faster production results. With the best Machine Learning software.

EPA reports generated automatically on a monthly basis from the SORBA IIoT-SCADA system scaled accordingly to thousands&millions of deployed sites.

Compressors are complex to maintain with proprietary controllers and communications. SORBA delivers a centralized AI platform reducing operating expenses.

Life science with SORBA-ML. A fully automated diagnostic dispensing machine used to process high volume biopsy tests.

Manufacturing metal with SORBA-ML. A Steel Manufacturing Plant Produces Rebar. 24 Hours a Day, 7 Days a Week. Auto ML would definitely save you time and money.

With SORBA Machine Learning System, you can get astonishing results such as predicted battery anomalies, reduce unscheduled downtime, and more.

SMART Asset Health Monitoring for Mining is developed to improve yield though immediate efficiency savings, utilization improvements and enhancements in both environmental and workforce safety.

In steel rolling mills, the rollers are housed in stands through which the steel billets pass to achieve a desired shape. Stable operating conditions for these stands are vital to achieving a good dimensional quality of the final product.

Solutions like SORBA.ai’s refrigeration application have a global energy savings potential surmounting 53B USD per year, while offsetting more than 198 million tons of annual CO2 emissions.

The refrigeration advanced process control (APC) solution is intended to be used by engineers, refrigeration technicians, and refrigeration consultants whose role is to improve the performance of refrigeration systems.

In the metals industry, reheating furnaces play a critical role in preparing materials for processing, but their high energy consumption presents a costly challenge. By applying AI-enabled Advanced Process Control (APC) models, early tests demonstrated up to 5% energy savings, with further optimization promising even greater efficiency and gas cost reductions.