QiO Technologies
Using AI to Optimize the Performance of Industrial Equipment

Industrial plants must continually refine operations to meet the ever-prevailing efficiency and sustainability goals. Their operators ought to collect and analyze a huge amount of data associated with the 'adjustments' they make daily while finding the ideal combination of settings. However, by the time they complete the data analysis, they are left with insufficient time to get back on track and implement the refinement measures to achieve desired outcomes. Also, as industrial operators are tasked with ensuring process stability, they can't afford to expend too much time doing the proverbial analysis and 'trial and error' of varied combinations of equipment and process settings to arrive at the energy efficiency and sustainability 'sweet' spot. To this end, QiO Technologies comes to the picture as an Industry 4.0 technology company built by industrial engineers and brings to the table the Foresight Sustainability Suite that offers automated and prebuilt AI solutions enabling clients to analyze their equipment configuration data on a near real-time basis.

QiO Technologies builds digital twins that operate in sync with an asset running in a factory and guide plant engineers and operators on exactly what set points to use to become more efficient. This ultimately delivers continuous systemic optimization that will allow clients to hit their net-zero and efficiency goals. "We always focus on granular, real-time recommendations. For instance, we can recommend an exact temperature controller on a particular equipment of a plant in minute or second level if necessary," says Yassine Houari, Head of Product, QiO Technologies.

Positioning itself as a consultative coach and mentor to clients, QiO Technologies' solution enables multi-asset analysis across billions of data points in real-time without any need for data scientists. This not only helps clients to achieve sustainability and continuous monitoring of their asset performance and progress but also enables them to identify the most valuable actions across maintenance and service. Clients can intervene quickly and repair and overhaul the assets in case there is any sign of degradation.

QiO Technologies' CTO Gary Chandler recalls a recent situation where he helped a glass manufacturer save significantly on operational costs.

The amount of energy used to convert raw material into glass products varied every day. The client was burning natural gas to run their furnace to produce over a hundred tons of glass products daily. Their operators had to constantly monitor and make adjustments to the control system to keep the furnace operating at the right temperature to ensure the highest quality product output with minimal energy consumption. By implementing prebuilt-AI tools on the control system of the furnace, QiO Technologies could recommend the exact configurations that the client needed to set at a certain point in time to achieve the desired temperature.

Driven by many such successes, QiO Technologies is currently working with BT Group and Intel that run numerous data centers across the globe to repurpose their AI-based asset performance optimization for data center environments. By Q2 of this year, the company plans to launch a product that will focus on energy optimization, growth, and sustainability, particularly for data centers.

We are helping the industrial sector to achieve predictability and remove uncertainty from production and operations by connecting complex industrial equipment, collecting the data siloed amongst systems, and leveraging this data to optimize operations

QiO Technologies continues to assist clients in making informed business value decisions by helping them understand which asset within the whole plant actually contributes to its overall performance. "We are helping the industrial sector to achieve predictability and remove uncertainty from production and operations by connecting complex industrial equipment, collecting the data siloed amongst systems, and leveraging this data mix to optimize operations," says Chandler.