PSI - Issue 77

Michal Holubčík et al. / Procedia Structural Integrity 77 (2026) 413– 423 “Mi chal Holubčík” / Structural Integrity Procedia 00 (2026) 000 – 000

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Efficiency losses, unstable operation Increased emissions, reduced lifetime Non-compliance with air quality standards Reduced efficiency and safety risks

Limited automation

Poor air–fuel ratio control Frequent cycling, unstable combustion PM and CO remain high Inconsistent maintenance, misuse

Dynamic heating loads

Lack of emission controls User operation practices

2. Control Methods for Small-Scale Biomass Boilers Control strategies are central to ensuring efficient and clean combustion in small-scale biomass boilers. Traditional methods provide only basic stability but fail to adapt to changing fuel and operating conditions. In recent years, model-based approaches and intelligent algorithms have been proposed, supported by IoT and low-cost sensors. This chapter provides an overview of conventional, model-driven, and data-driven control methods, highlighting their principles, strengths, and limitations, as a foundation for developing smart regulation systems. 2.1. Conventional Control Approaches Covers on/off (start–stop) and classic PID regulation. These are simple and inexpensive but limited in adapting to variable fuel quality and dynamic loads. The simplest regulation in small-scale biomass boilers is on/off (start– stop) control , where the feeder and fan operate intermittently to maintain water temperature. While inexpensive, this approach results in frequent cycling, thermal losses, and efficiency reductions of up to 10–15% . More advanced is the PID controller , which continuously adjusts air o r fuel supply based on feedback. Although widely used in industry, domestic biomass units often implement only proportional (P) or PI variants, leading to overshoot and slow response under variable loads. Studies show that PID-based systems can reduce CO emissions by 20–30% compared to on/off operation yet remain limited in adapting to fuel variability and dynamic conditions [4].

a) b) Figure 2 : Comparison of steam boiler operation using a) traditional start/stop-based control b) Intelligent control based on fuzzy logic [5] 2.2. Model-Based Control Model-based control methods represent an intermediate step between conventional PID strategies and fully intelligent algorithms. Their principle lies in using mathematical models of the combustion process to predict future states and adjust the air–fuel ra tio accordingly. Unlike start –stop regulation, which often causes efficiency losses of 10–15%, model-based methods can stabilize operation and reduce emissions more effectively. Ratio control is one of the simplest examples, maintaining a predefined link b etween fuel feed and airflow. For biomass, the stoichiometric requirement is approximately 4.5– 5.5 kg of air per kg of dry fuel.