No-code industrial optimization platform

Optimizing industrial processes through Digital Twins and Machine Learning models. Reduce energy consumption and improve output KPIs in days instead of months.

Digital Twin

The Digital Twin is an accurate simulation of the physical and chemical properties of an industrial process. It can be built through a simple Drag&Drop interface without requiring coding skills

 

Simulation environment

By using the Digital Twin, we can accurately simulate an industrial process and design scenarios with different hardware for optimal strategic planning.

Machine Learning training ground

The Digital Twin is being used as a safe and accurate training ground for Machine Learning optimizations and Predictive Maintenance

Fixing insufficient data problem

Sometimes there isn't collected data for every manufacturing process or the data is unreliable due to faulty/uncalibrated sensors. The Digital Twin fixes those issues and allows for the development of optimizations even with such conditions.

Controls and automates the process

A Reinforcement Learning agent that dynamically controls the industrial process at any moment in order to achieve optimal energy consumption and output KPIs. 

Optimal energy consumption and output KPIs

By dynamically controlling how the motor in an industrial process works at any moment, the Machine Learning algorithm saves energy and improves output KPIs, while adapting itself to different conditions. The algorithm achieves this by deciding how the motors should work (e.g at what frequency), and when should the motors work based on energy prices, internal schedules, etc. 

No-code capabilities

The engineers can define a reward function by specifying a series of human-understandable KPIs which must be minimized or maximized. Alternatively, Apprenticeship Learning and Generative Adversarial Imitation allow the system to develop RL agents by mimicking the actions of operators allowing for more complex problems to be solved without the need for expert knowledge.

Machine Learning
optimization

The platform allows industrial optimization problems to be modeled as Markov Decision Process (MDP) which can be solved using deep Reinforcement Learning.

 

Use cases

Mills

By controlling the milling parameters, our Optimization models improve the extraction rate and reduce energy consumption during the milling process. Extraction improvement up to 4% and reducing energy consumption by up to 13% can be expected. 

Application: Sugar cane milling, flour milling, fruit juice extraction process.

Industrial Furnaces

Optimize burner schedule to reduce gas usage, electricity usage(for air compressors that feed air into the burner), and GHG emissions by achieving an optimal air-fuel ratio. 

Applications: Enamelling, steel manufacturing, Ceramic and glass baking, Brewery.

Pumps

Save up to 30% of the energy consumption used for your pumping operations by applying our system. It dynamically controls the working frequency of the pumps in order to achieve optimal results.

Applications: Water utilities, Irrigation, Mining, Steel manufacturing, Brewery, Plastic.

HVAC

Reduce costs for heating and cooling while maintaining an optimal thermal comfort index by selecting when to turn chillers/heaters on/off or to pull air from the outside to adjust humidity based on energy price and expected energy usage and the occupancy rate of the building + weather conditions

Applications: Commercial buildings, business buildings, large venues, industrial facilities.

 

About

Vision

Technology

We believe that within every manufacturer, there are engineers who know the ins and outs of the processes they've been working on every day. With the right toolset, these engineers will be able to quickly and efficiently optimize those processes, just by using a software solution and without the need for external expertise. This will result in a manufacturing industry that is quickly moving forward with its digitalization and innovation.

Our team has successfully worked on optimizing industrial processes, and we've seen the great capabilities and knowledge of the people who are working every day with those processes. That's why we implemented our optimization methodology into a product that can be used by engineers without programming knowledge. We implemented a state-of-the-art Digital Twin and Machine Learning technology into a product that can transform the way optimizations are conducted and greatly increase the speed of implementing innovative projects into day-to-day operations. 

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