Termosun, Pervasive and Imae join forces in an R&D&I project for biomass

Termosun , Pervasive and Imae, in collaboration with Schneider, join their resources and knowledge in a new R&D&I project for the optimization of the combustion of biomass and related byproducts in industrial boilers through the Machine Learning and Big Date.

The notable increase in the prices of gas of fossil origin, as well as the increase in the price of carbon credits, are pushing a large number of industries in Spain towards the conversion of their thermal plants, replacing the traditional and obsolete gas boilers. by boilers that use renewable biofuels, such as forest biomass and agroindustrial byproducts.

There is, therefore, a growing demand for biomass boilers in the food, automotive, chemical, etc. industries . But at Termosun we observe that industrial operators and maintainers do not have sufficient skills to manage boilers, ensuring maximum energy efficiency, as well as minimum emission of pollutants.

The management in the operation and maintenance of biomass boilers is superior to the simplicity of operation of gas boilers and this technological leap must be supported by advanced control technologies, avoiding leaving boiler efficiency in many cases by virtue of the operator.

On the other hand, they ensure that the useful life of the boilers is not reduced or even suffers from defects that cause emissions into the atmosphere or overconsumption of biomass, making it necessary to work with advanced data acquisition and control technologies for this equipment to guarantee energy conservation and environmental protection.

The solution under study that Termosun is developing in collaboration with Pervasive and Imae, with the name “3BD – Biomass Boiler Big Data”, aims to offer the biomass boiler market a solution to guarantee functional performance in terms of energy performance, reduction of impacts and reduction of operation and maintenance costs.

Under the toolbox concept, this project is conceived as a combination of tools that integrate into the boiler in different scalability and licensing layers, the basic tools being:

• Continuous measurement of the parameters that occur in the different physical-chemical states during the combustion process,
• Image capture of combustion grill inside oven
• Exhaust gas inline oxygen and temperature sensor set
• Massive data acquisition and interpretation platform
• Digital training and data-related model and self-learning
• Operation parameter correction interface

As shown in the following image, these tools are organized into different layers, starting with layer 0, also known as the field hardware layer, and progressively scaling the information to higher layers of acquisition, interpretation, iteration and alteration. of operating parameters to finally culminate with the report to the technical assistance service.

The 3BD (Biomass Boiler Big Data) project was born with the aim of improving current algorithm models through Machine Learning treatments, a discipline in the field of Artificial Intelligence that, through obtaining twins and creating algorithms, allows us to identify patterns in massive data Big Data to prepare predictions that allow the operation of the boiler to be digitalized for optimal combustion, performance and minimum emissions.