This article presents an intelligent design approach for frequency-controlled small-scale heating systems, incorporating low-cost sensors to enhance efficiency and reduce emissions. It begins with a literature review focusing on the use of modern sensors and frequency control technologies for regulating fan and fuel speeds. As part of the review, a basic outline of neural networks applied in biomass combustion systems is also provided, highlighting their potential for optimizing combustion and emission control through adaptive, data-driven strategies. The proposed smart control system is built around dual Raspberry Pi units, supported by Arduino Uno R3 and Arduino Nano microcontrollers, chosen for their compatibility with available sensor libraries. The frequency control consists of two IVT 0,75kW converters. The system integrates a range of low-cost environmental sensors from DFRobot, including the SCD41 CO₂ sensor, an electrochemical oxygen sensor, an ozone sensor, the ENS160+BME280 environmental sensor (measuring air quality, temperature, humidity, and pressure), a separate temperature and humidity sensor, and a laser-based particulate matter (PM) sensor for total suspended particles (TSP).The fan and pellet feeder are driven by frequency inverters connected via an FT232RL USB to RS485 adapter, allowing precise real- time control based on sensor feedback. The article presents detailed hardware and software requirements, followed by a step-by-step methodology for constructing and testing the system. Special emphasis is placed on the integration of these components into an experimental boiler setup, demonstrating how intelligent regulation can be practically implemented in low-cost biomass heating applications to improve combustion efficiency and reduce harmful emission.
Nicolanská et al. (2025) studied this question.