enerGQ’s Artificial Energy Intelligence (Aei^®) software technology uses the holistic nature of energy as the only all-encompassing process parameter to detect performance optimization opportunities in production facilities.

 

 

The software combines time series data with context and domain knowledge and learns what the normal energy usage is in a variety of process and weather conditions.  The software then generates the insights for optimizations on a need-to-know basis, using the deviations from normal energy usage, in near real time. The software acts on plant level, process unit level and individual equipment level. The software interacts with OSIsoft-PI and almost every other existing data-infrastructure system.

Our technology is modular and can therefore be seamlessly linked to systems such as OSIsoft-PI.

 

No energy data available? No problem. We can request data from your measuring company and, if desired, install additional equipment to monitor your installations in more detail (see also: Hardware).

 

Our technology is the result of years of cooperation with customers for the application and continuous development of our unique AEI-based software, development of our technical expertise, the deployment of our cost-effective energy monitoring equipment and the secure connection with practically all data systems and industry standards.

Key features

• Baselining at all levels: plant, process unit and individual equipment.
• Energy savings potential scan from historical data at plant level as input for the development and implementation of business cases.
• Minimizing the use of energy, raw materials and waste production from using the best setpoints combinations within the normal window of operations.
• Better continuity of operations. The software detects irregularities at a very early stage, to prevent escalation and optimize maintenance.
• Energy submetering for the monitoring of energy usage and power quality. Easy to install during operations.
• Offline optimization using digital twinning principles.
• Determining the amount of energy saved. Comparing the energy usage before and after modifications under similar operational and weather conditions.
• Planning and checking energy savings of other energy saving technologies.

 

Application fields

Industry, chemical industry, petrochemical industry, water and wastewater, maritime, paper and pulp, food and beverage industry, mining and cement industry, utility, infrastructure, transportation and other sectors where energy is used or produced in the form of electricity, (bio)gas, steam, heat, cold or kerosene.

 

Business case

A biomedical company invested in submetering. By using the software, various settings have been adapted that resulted in 15% energy savings equal to 340 ton CO₂/year with an energy cost saving value of about € 75,000 per year. Pay-back period (including the learning period of 6 months) was less than 2 years. The savings of this case have been validated against the learned (normal) consumption model, and by temporarily restoring the old settings and measuring the increase in consumption.

CO₂ impact / Energy cost reduction

• Energy savings (5%-30%)
• More effective maintenance, higher reliability and less downtime, lower energy costs, less raw material consumption and less occasions of off-spec production

Non-energy benefits

• Better operations due to fewer failures
• Safeguarding knowledge
• Development of new insights and awareness